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Statistics

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DATA PRESENTATION
SUMMERIZATION
NORMAL DISTRIBUTION CURVE
INFERENTIAL STATISTICS

Statistics

  1. 1. STATISTICSSTATISTICS Prof. Dr. Mona Aboserea
  2. 2. Definition: Statistics is the science of dealing with numbers. It is used for collection, summarization, presentation, and analysis of data. Uses:  Planning & evaluation of health care programs. Play a role in epidemiological studies. Diagnosis of community health problems. Comparison of diseases and health status. Forming standards for biologic measurements e.g. BP. Differentiation between diseased and normal groups.
  3. 3. 7-3 Data CollectionData Collection
  4. 4. Data : Observations made on individuals. Variable : any aspect of individual that is measured e.g. blood pressure, age.
  5. 5. Confounding variable: are two variables (explanatory variables) that are confounded when their effects on a response variable cannot be distinguished from each other
  6. 6. Confounding variablesConfounding variables Drinking Coffee Pancreatic Cancer Drinking Coffee Smoking Cigarettes Pancreatic Cancer The relationship between coffee drinking and pancreatic cancer is confounded by cigarette smoking.
  7. 7. Types of dataTypes of data Quantitative Qualitative DiscreteDiscrete datadata‫تتتتتت‬‫تتتتتت‬ ‫تتتت‬‫تتتت‬ ‫تتتتتت‬‫تتتتتت‬ ContinuousContinuous datadata‫تتتت‬‫تتتت‬ ‫تت‬‫تت‬ ‫تتتت‬‫تتتت‬ ‫تتتتت‬‫تتتتت‬ CategoricalCategorical OrdinalOrdinal No. of hospitalsNo. of hospitals No. of patientsNo. of patients Pulse ratePulse rate WeightWeight HeightHeight AgeAge Blood group: A, B,Blood group: A, B, AB & OAB & O Male & femaleMale & female Social class:Social class: low, middle,low, middle, & high& high
  8. 8. Presentation of Data Tabular Presentation . Graphical Presentation .
  9. 9. I. Tabulation:I. Tabulation: criteria‫تتتت‬ ‫تتتتتت‬  Self explanatory.Self explanatory.  Title at the top.Title at the top.  Clear headings of columns and rows.Clear headings of columns and rows.  Clear units of measurements.Clear units of measurements.  Number of classes or rows from 2-10.Number of classes or rows from 2-10.  2 types :2 types :  Listing tables.Listing tables.  Frequency distribution table.Frequency distribution table.
  10. 10. No. of patients in each department at Zagazig hospitalNo. of patients in each department at Zagazig hospital Department No. of patientsNo. of patients Medicine Surgery ENT Ophthalmology 100100 8080 4040 3030 Total 250250 (1) Listing table(1) Listing table
  11. 11. Distribution of students at public health lab 1 according to gender Gender No. of studentsNo. of students Male Female 3535 2020 Total 5555 e.g. Listing tablee.g. Listing table
  12. 12. (2) Frequency distribution table for(2) Frequency distribution table for qualitativequalitative data:data: 20 individuals of blood group: A- AB- AB-O-B-A-20 individuals of blood group: A- AB- AB-O-B-A- A-B-B-AB-O-AB-AB-A-B-B-B-A-O-A.A-B-B-AB-O-AB-AB-A-B-B-B-A-O-A. Distribution of the studied individuals according to their blood group. Blood group FrequencyFrequency %% A B AB O 66 66 55 33 3030 3030 2525 1515 TotalTotal 2020 100.00100.00
  13. 13. (3) Frequency Distribution table for(3) Frequency Distribution table for quantitative data example:example: Blood Pressure ofBlood Pressure of 30 patients with30 patients with hypertension are:hypertension are: 150-155-160-154-162-170--155-160-154-162-170- 165-155-190-186-180-178-195-165-155-190-186-180-178-195-200-180-165--180-165- 173-188-173-189-190-175-186-174-155-164-173-188-173-189-190-175-186-174-155-164- 163-172-159-177.163-172-159-177. Present these data in a frequency table?Present these data in a frequency table?
  14. 14. 1.1. Title:Title: 2.2. Table: 3 columns :Table: 3 columns : 11stst : blood pressure: blood pressure  22ndnd : Frequency.: Frequency. 33rdrd : Percentage.: Percentage. 3.3. First column: classify blood pressure into classes.First column: classify blood pressure into classes. 4.4. Choose a class interval: 10.Choose a class interval: 10. 5.5. No. of classes=50 (largest value-lowestNo. of classes=50 (largest value-lowest value)/10=5.value)/10=5. 6.6. Choose uper & lower limit of the class interval.Choose uper & lower limit of the class interval. 7.7. Each observation allocated to its class interval.Each observation allocated to its class interval. 8.8. Percentage of each class is calculated.Percentage of each class is calculated.
  15. 15. Frequency Distribution of blood pressure measurements among the studied group: Blood PressureBlood Pressure mmHgmmHg FrequencyFrequency %% TallyTally No.No. 150-150- 160-160- 170-170- 180-180- 190-190- 200-200- |||| ||||| | |||| ||||| | |||| ||||||| ||| |||| ||||| | |||||| || 66 66 88 66 33 11 2020 2020 26.726.7 2020 1010 3.33.3 TotalTotal 3030 100.00100.00
  16. 16. II- Graphical Presentation DefinitionDefinition:: Presenting data by using diagrams.Presenting data by using diagrams. Graph should be : Simple, understood.Simple, understood. Save a lot of words.Save a lot of words. Self explanatory.Self explanatory. Clear title.Clear title. Fully labeled.Fully labeled. Vertical axis used for frequency.Vertical axis used for frequency.
  17. 17. Bar chart  Used forUsed for discrete oror qualitativequalitative data.data.  Data presented by rectangles separated byData presented by rectangles separated by gaps,gaps, the length is proportional to the frequency..  Types of Bar charts:Types of Bar charts:  Simple.Simple.  Multiple.Multiple.  ComponentComponent..
  18. 18. Simple bar chartSimple bar chart Blood gp.Blood gp. Freq.Freq. AA BB ABAB OO 44 88 55 33 TotalTotal 2020 4 8 5 3 0 1 2 3 4 5 6 7 8 9 A B AB O Blood Group Frequency
  19. 19. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 41% 47% 75% 5% 9.50% 2.90% 1.60% 58% 87.50% risk factors Associated risk factors for type 2 DM among the studied patients
  20. 20. Multiple bar chartMultiple bar chart Blood gp.Blood gp. Freq.Freq. Female MaleFemale Male AA BB ABAB OO 33 66 77 44 44 88 55 33 TotalTotal 2020 2020 What is the defect in this char???????????
  21. 21. SE ClassSE Class %% Egypt USAEgypt USA LowLow MiddleMiddle HighHigh 6060 3030 1010 1010 6060 3030 TotalTotal 100100 100100 Component Bar Chart FrequencyFrequency %% SE ClassSE ClassEgyptEgypt USAUSA 2020 4040 6060 8080 100100 What is the defect in this char???????????
  22. 22. Pie Chart  Circle represent the total frequency 100%.  Used in discrete or qualitative data.  Divided into segments according to the proportion of each category.  2 pies can be used for comparison.
  23. 23. Disease % Diarrhoea Chest infection Congenital Accidents 50 30 10 10 Total 100 50% 30% 10% 10%
  24. 24. Gender Distribution of the studied patients at diabetic center. Female Male
  25. 25. Histogram ::  Used forUsed for quantitative continuous data.quantitative continuous data.  Each class interval represented byEach class interval represented by rectangle.rectangle.  The height ofThe height of rectangle represent therepresent the frequency.frequency.  Rectangles areRectangles are adherent.
  26. 26. ht (cm)ht (cm) Freq.Freq. 100-100- 110-110- 120-120- 130-130- 140-140- 150-160150-160 1010 1414 2525 1717 1515 88 -100-100-110-110-120-120-130-130-140-140160 - 150160 - 150 Frequency
  27. 27. Frequency PolygonFrequency Polygon::  Derived fromDerived from histogram..  The midpoint of the rectangles’The midpoint of the rectangles’ top are connected.top are connected.  It can be drawn withoutIt can be drawn without histogram.histogram.
  28. 28. -100-100-110-110-120-120-130-130-140-140160 - 150160 - 150
  29. 29. 0 5 10 15 20 25 30 100-110-120-130-140-150-160
  30. 30. Scatter Diagram  Used to represent the relationshipUsed to represent the relationship betweenbetween 2 quantitative continuous measurements.measurements.  Each observation is represented by a pointEach observation is represented by a point corresponding to its value on each axis.corresponding to its value on each axis.
  31. 31. 1.1. If the points scatterIf the points scatter upward directionupward direction +ve correlation. 2.2. If the point scatterIf the point scatter downwarddownward direction –ve correlation. 3.3. If the points scatterIf the points scatter horizontallyhorizontally nono correlation.correlation.
  32. 32. Line Graph  Represent the relationship between 2Represent the relationship between 2 numeric variables.numeric variables.  The points joined together to from aThe points joined together to from a line.line.  Ex: Relation between temperature & time.Ex: Relation between temperature & time.  Relation between height & weight.Relation between height & weight.  Line graphs can be used for more thanLine graphs can be used for more than one group.one group.
  33. 33. Temperature 36 36.5 37 37.5 38 38.5 39 39.5 1 2 3 4 5 6 7 Time (hrs)Time (hrs)
  34. 34. 70 75 80 85 90 95 100 1 2 3 4 Time in days HR Drug 3Drug 3 Drug 2Drug 2 Drug 1Drug 1
  35. 35. Graphical Presentation  Qualitative & discrete data:Qualitative & discrete data: * Bar Chart* Bar Chart * Pie chart* Pie chart  Quantitative continuous data:Quantitative continuous data:  Histogram (e.g. population pyramid).Histogram (e.g. population pyramid).  Frequency polygon (e.g. normal distribution curve)Frequency polygon (e.g. normal distribution curve)  Relation between 2 numerical variables:Relation between 2 numerical variables:  Scatter diagram.Scatter diagram.  Line graph.Line graph. Remember
  36. 36. While preparing the report of gastroenteritis outbreak investigation the researcher wanted to present the data i.e. number of cases related to time, graphically. Which graph would you suggest? a) Bar chart b) Pictogram c) Pie chart d) Histogram e) Scatter diagram
  37. 37. Thank youThank you
  38. 38. Data SummarizationData Summarization Measures ofMeasures of central tendencycentral tendency Measures ofMeasures of dispersiondispersion  Arithmetic meanArithmetic mean ..  MedianMedian ..  ModeMode .. RangeRange Variance.Variance. Standard deviation.Standard deviation. Coefficient ofCoefficient of variation.variation.
  39. 39. I- Measures of central tendencyI- Measures of central tendency  Describe the center of data:Describe the center of data: X = meanX = mean  = sum= sum X = value of observations.X = value of observations. n= number of observations.n= number of observations. 1.1. Ungrouped data: 12, 15, 10, 17, 13.Ungrouped data: 12, 15, 10, 17, 13. = 12+15+10+17+13/5 = 13.4= 12+15+10+17+13/5 = 13.4 n X X ∑= n X X ∑= n X X ∑= n X X ∑=
  40. 40. 2. Grouped data without class interval:2. Grouped data without class interval: Where f = frequency of each XWhere f = frequency of each X n X X ∑= n X X ∑= n fX X ∑= IP (days)(x)IP (days)(x) Freq. (f)Freq. (f) FxFx 22 33 44 55 66 22 44 11 33 22 44 1212 44 1515 1212 TT 12 (n)12 (n) 47 (47 (fx)fx) X IP = 74/12 = 3.9 days.X IP = 74/12 = 3.9 days.
  41. 41. 3. Frequency data with class interval:3. Frequency data with class interval: X1 = midpoint of class interval.X1 = midpoint of class interval. n X X ∑= n X X ∑= Bl. PressureBl. Pressure mmHg (x)mmHg (x) Freq. (f)Freq. (f) Midpoint (xMidpoint (x11)) FxFx11 150-150- 160-160- 170-170- 180-180- 190-190- 200-210200-210 66 66 88 66 33 11 155155 165165 175175 185185 195195 205205 930930 990990 14001400 11101110 585585 205205 TT 30 (n)30 (n) 5220 (5220 (fxfx11)) * Mean blood pressure = 5220/30= 174 mmHg.* Mean blood pressure = 5220/30= 174 mmHg. n fX X ∑= 1
  42. 42. (2) Median :(2) Median :  Median is the middle observation in a series ofMedian is the middle observation in a series of observations after arranging them in an assending orobservations after arranging them in an assending or dessending manner.dessending manner. 1. If no. of observation is odds:1. If no. of observation is odds:  A set of data 5,6,8,9,11A set of data 5,6,8,9,11 n=5n=5  Median rank = n +1/2 = 5+1/2 = 3Median rank = n +1/2 = 5+1/2 = 3  Median is the third value (8).Median is the third value (8). 2. If no. of observations is even:2. If no. of observations is even:  A set of data 5,6,8,9A set of data 5,6,8,9 n=4n=4  Median rank = 4+1/2= 5/2= 2.5.Median rank = 4+1/2= 5/2= 2.5.  Median is the average of second & third value =Median is the average of second & third value = 6+8/2= 14/2= 7.6+8/2= 14/2= 7.
  43. 43. Mode :Mode :  The most frequent value.The most frequent value.  Example:Example:  5,6,7,5,105,6,7,5,10 mode = 5mode = 5  20,18,14,20,13,14,3020,18,14,20,13,14,30 mode= 14,20mode= 14,20  20,18,20,14,20,13,1420,18,20,14,20,13,14 mode = 20mode = 20  300,280,130,125,24300,280,130,125,24 No modeNo mode
  44. 44. II- Measures of dispersion:II- Measures of dispersion:  Describe the degree of variation of dataDescribe the degree of variation of data around the central values:around the central values: 1. Range = largest observation – smallest observation.1. Range = largest observation – smallest observation. (mean-x)(mean-x)22 2. Variance (V) = ----------------------2. Variance (V) = ---------------------- n – 1n – 1 n X X ∑= n X X ∑=
  45. 45. 3. Standard deviation (SD):3. Standard deviation (SD): (X-X)(X-X)22 SD = V = -------------SD = V = ------------- n-1n-1 4. Coefficient of variation (CV)4. Coefficient of variation (CV) The percentage of SD from the meanThe percentage of SD from the mean CV = SD/mean x 100CV = SD/mean x 100 n X X ∑= n X X ∑=
  46. 46. ExampleExample 1. Set of observation 5, 7, 10, 12, 161. Set of observation 5, 7, 10, 12, 16 X = 5+7+10+12+16/5 = 50/5 = 10X = 5+7+10+12+16/5 = 50/5 = 10 (10-5)(10-5)22 ++ (10-7)(10-7)22 +(10-10)+(10-10)22 +(10-12)+(10-12)22 +(10-16)+(10-16)22 7474 SD= -------------------------------------------------------- = ------- = 4.3SD= -------------------------------------------------------- = ------- = 4.3 5 – 15 – 1 44 CV = 4.3/10 x 100 = 43%CV = 4.3/10 x 100 = 43% 2. Set of observations 2, 2, 5,10, 112. Set of observations 2, 2, 5,10, 11 X = 2+2+5+10+11/5 = 30/5 = 6X = 2+2+5+10+11/5 = 30/5 = 6 (6-2)(6-2)22 +(6-2)+(6-2)22 +(6-5)+(6-5)22 +(6-10)+(6-10)22 +(6-11)+(6-11)22 7474 SD= -------------------------------------------------------- = ------- = 4.3SD= -------------------------------------------------------- = ------- = 4.3 5 – 15 – 1 44 CV = 4.3/6 x 100 = 71.6%CV = 4.3/6 x 100 = 71.6%
  47. 47. Histogram with Normal Distribution Curve
  48. 48. Frequency polygon with Normal Distribution Curve
  49. 49. Normal Distribution CurveNormal Distribution Curve (Guassian Curve)(Guassian Curve)  A frequency polygon used in presentationA frequency polygon used in presentation continuous quantitative variables as age,continuous quantitative variables as age, weight, height, Hb level, bl. pressure.weight, height, Hb level, bl. pressure.  Normal distribution curve is used to identifyNormal distribution curve is used to identify normal & abnormal measurements.normal & abnormal measurements.
  50. 50. Characteristics of the CurveCharacteristics of the Curve  Bell-shaped, continuous.Bell-shaped, continuous.  Symmetrical.Symmetrical.  The tail extend to infinity.The tail extend to infinity.  Mean, mode, median coincide.Mean, mode, median coincide.  Described by:Described by: - arithmatic means ( )- arithmatic means ( ) - standard deviation (SD)- standard deviation (SD)  Area under the normal curve:Area under the normal curve: ± 1 SD = 68%± 1 SD = 68% ± 2 SD = 95%± 2 SD = 95%  the normal rangethe normal range ± 3 SD = 99%± 3 SD = 99% X X X X
  51. 51. Distribution of DataDistribution of Data
  52. 52. Example:Example:  In normal distribution curve for blood HbIn normal distribution curve for blood Hb level for normal adult ♂:level for normal adult ♂: Mean = 11Mean = 11 SD= ± 1.5SD= ± 1.5  Hb of an individual is 8.1 is he normal orHb of an individual is 8.1 is he normal or anaemic?anaemic?  The higher level of Hb = 11+2 x 1.5 = 14The higher level of Hb = 11+2 x 1.5 = 14  The lower level of Hb = 11- 2 x 1.5 = 8The lower level of Hb = 11- 2 x 1.5 = 8  The normal range of Hb in adult ♂ is 8-14The normal range of Hb in adult ♂ is 8-14  Our patient (8.1) is normal.Our patient (8.1) is normal.
  53. 53. Thank youThank you
  54. 54. Inferential Statistics Prof. Dr. Mona Aboserea
  55. 55. N.B.N.B. Research ProcessResearch Process Research question Hypothesis Identify research design Data collection Presentation of data Data analysis Interpretation of data
  56. 56. What is a Statistic???? Population Sample Sample Sample Sample Parameter: value that describes a population Statistic: a value that describes a sample  always using samples!!!
  57. 57. Statistics Descriptive Statistics • Organize • Summarize • Simplify • Presentation of data Inferential Statistics •Generalize from samples to pops •Hypothesis testing •Relationships among variables Describing data Make predictionsMake predictions
  58. 58. Inferential Statistics .
  59. 59. Inferential StatisticsInferential Statistics  Inferential statistics are used to draw conclusions about a population by examining the sample POPULATION Sample
  60. 60. Sample Inference Population
  61. 61.  Inference:Inference: making a generalization about amaking a generalization about a larger group of population on the basis of alarger group of population on the basis of a sample.sample.  Inferential statistics Instead of using the entire population to gather the data, the statistician will collect a sample or samples from the millions of residents and make inferences about the entire population using the sample.
  62. 62.  Hypothesis (significance) testing:Hypothesis (significance) testing: Conducting of significance test to find outConducting of significance test to find out whether the observed variation among sampling iswhether the observed variation among sampling is due todue to chance or it is a really difference.chance or it is a really difference.
  63. 63. General principles (steps) of significance tests  Set up the null hypothesis & its alternative.Set up the null hypothesis & its alternative.  Set level of significance:Set level of significance: In medicine, we consider the difference are significantIn medicine, we consider the difference are significant if the probability (P value) is less thanif the probability (P value) is less than 0.05.  Find theFind the value of the test statistics (calculatedvalue of the test statistics (calculated value)value)..
  64. 64. General principles (steps) of significance tests  Find the tabulated value.Find the tabulated value.  Conclude that the data are consistent orConclude that the data are consistent or inconsistent with theinconsistent with the null hypothesis byby comparing the two values. If data are notcomparing the two values. If data are not consistent with null hypothesis we rejectconsistent with null hypothesis we reject it & the difference isit & the difference is statistically significant & the vice versa.& the vice versa.
  65. 65. Null & alternative hypothesis For quantitative data  In null hypothesis (H0): X1=X2 or X1-X2=0.  Alternative hypothesis (H1) is postulated (Research hypothesis). H1 : X1<X2 or H1: X2<X1. or X1 ≠ X2 or X1-X2 ≠ 0
  66. 66. N.B. Statistics demonstrate association, but not causation H0: There is no association between the exposure and disease of interest H1: There is an association between the exposure and disease of interest 74 Hypothesis Testing For qualitative data
  67. 67. Chain of Reasoning for Inferential Statistics Population Sample Inference Selection Measure Probability data Are our inferences valid?…Best we can do is to calculate probability about inferences
  68. 68. Inferential Statistics: uses sample data to evaluate the credibility of a hypothesis about a population NULL Hypothesis: NULL (nullus - latin): “not any”  no differences between means H0 : m1 = m2 “H- Naught”Always testing the null hypothesis
  69. 69. Inferential statistics: uses sample data to evaluate the credibility of a hypothesis about a population Hypothesis: Scientific or alternative hypothesis Predicts that there are differences between the groups H1 : m1 = m2
  70. 70. Hypothesis A statement about what findings are expected null hypothesis "the two groups will not differ“ alternative hypothesis "group A will do better than group B" "group A and B will not perform the same"
  71. 71. Inferential Statistics When making comparisons btw 2 sample means there are 2 possibilities Null hypothesis is true Null hypothesis is false Not reject the Null Hypothesis Reject the Null hypothesis Statistical significanceNo Statistical significance
  72. 72. D+ D- E+ 15 85 E- 10 90 Example: IE+ = 15 / (15 + 85) = 0.15 IE- = 10 / (10 + 90) = 0.10 RR = IE+/IE- = 1.5, p value = 0.30 Although it appears that the incidence of disease may be higher in the exposed than in the non-exposed (RR=1.5), the p-value of 0.30 exceeds the fixed alpha level of 0.05. This means that the observed data are relatively compatible with the null hypothesis. Thus, we do not reject H0 in favor of H1 (alternative hypothesis).
  73. 73. 2.5% 2.5% 5% region of rejection of null hypothesis Non directional Two Tail
  74. 74. 5% 5% region of rejection of null hypothesis Directional One Tail
  75. 75. N.B.N.B. In medicineIn medicine We consider that differences are significant if the probability (p value) is less than 0.05 this means that:  if the null hypothesis is true, we will make aif the null hypothesis is true, we will make a wrong decision less than 5 in a hundredwrong decision less than 5 in a hundred times.times.
  76. 76. Hypothesis Testing Flow ChartHypothesis Testing Flow Chart Develop research hypothesis H1 & null hypothesis H0 Set significance level (usually .05( Collect data Calculate test statistic and p value Compare p value to alpha (.05( P < .05 P > .05 Reject null hypothesis Fail to reject null hypothesis Statistical significance No Statistical significance
  77. 77. Tests of significance are methods to assess the hypothesis testing
  78. 78. Matched Groups Paired t test
  79. 79. ((A) Quantitative dataA) Quantitative data 1.1. Compare 2 means of large sample (≥60) & followCompare 2 means of large sample (≥60) & follow normal distributionnormal distribution  Z testZ test (SND)(SND) == (population mean – sample mean)/SD(population mean – sample mean)/SD
  80. 80. If the result of Z >2 then there is significant difference. As we mentioned before the normal range for any biological reading lies between the mean value of the population reading ± 2 SD. (this range includes 95% of the area under the normal distribution curve).
  81. 81. 2. Compare 2 means of small sample (<60)2. Compare 2 means of small sample (<60)   tt test =test = df=ndf=n11+n+n22 -2-2  The value ofThe value of tt is compared to the values inis compared to the values in t-tablet-table at the value of degree of freedom.at the value of degree of freedom. 2 2 2 1 2 1 21 n SD n SD xx + −
  82. 82.  TheThe value of tvalue of t will be compared to values in thewill be compared to values in the specific table ofspecific table of "t distribution test""t distribution test" at theat the value of the degree of freedom.value of the degree of freedom.  If the value ofIf the value of tt isis less thanless than that in the table,that in the table, then the difference between samples isthen the difference between samples is insignificant.insignificant.  If theIf the t valuet value isis larger thanlarger than that in the table sothat in the table so the difference is significant i.e.the difference is significant i.e. the nullthe null hypothesis is rejected (significant).hypothesis is rejected (significant).
  83. 83. Serum cholesterol levels for two groups of EgyptiansSerum cholesterol levels for two groups of Egyptians were recorded. The mean cholesterol levels of thewere recorded. The mean cholesterol levels of the two groups were compared. To determine whethertwo groups were compared. To determine whether the measurements were significantly different or not,the measurements were significantly different or not, the most appropriate statistical test would be:the most appropriate statistical test would be: a. Chi-square testa. Chi-square test b. Correlation analysisb. Correlation analysis c. F test (ANOVA)c. F test (ANOVA) d. Student’s t testd. Student’s t test e. Regression analysise. Regression analysis
  84. 84. In a study carried out to assess the hemoglobin level of two groupsIn a study carried out to assess the hemoglobin level of two groups of students, one group of them was suffering from parasiticof students, one group of them was suffering from parasitic infestation.infestation. The following was found out:The following was found out: Group1 Healthy )Hb level( Group2 parasitic infestation )Hb level( 12 10 13 9 16 12 13 11 15 8 16 10.5 15 11 14 9.5 14 13 11 11 Is there a statistical significant difference between the two groups? )P value < 0.05 if test results > 2.11 ( Tabulated value
  85. 85. AnswerAnswer
  86. 86. 3-Paired t test:3-Paired t test:  Compare Means of twoCompare Means of two matched samplesmatched samples oror means of repeated observation in the samemeans of repeated observation in the same individualindividual )Pre & post()Pre & post(..  Paired t-test =the mean difference divided byPaired t-test =the mean difference divided by )standard deviation difference between each pair ∕)standard deviation difference between each pair ∕ √√n(n(
  87. 87. Six volunteers took a cholesterol lowering diet for 3Six volunteers took a cholesterol lowering diet for 3 months and mean cholesterol levels were measuredmonths and mean cholesterol levels were measured beforebefore andand afterafter the trial diet. The appropriate test ofthe trial diet. The appropriate test of statistical significance for this trial will be:statistical significance for this trial will be: a) Chi-square testa) Chi-square test b) Odd’s ratiob) Odd’s ratio c) Paired t- testc) Paired t- test d) Student t-testd) Student t-test e) Z tesTe) Z tesT
  88. 88. 4-Analysis of variance )ANOVA = F test(: Comparing several means: D-F = (d.f between groups, df within groups)D-F = (d.f between groups, df within groups) = K – 1, N – K= K – 1, N – K Mean square difference between groups F= Mean square difference within groups
  89. 89.  A-One way analysis of variance:A-One way analysis of variance: It is used toIt is used to compare means of more than 2 groups by a definedcompare means of more than 2 groups by a defined one factorone factor e.g.e.g. )BG in 3 groups of pts: 1-lifestyle,)BG in 3 groups of pts: 1-lifestyle, 2-OHA, 3-Insulin therapy(2-OHA, 3-Insulin therapy(
  90. 90. e.g. Comparing mean blood glucose levels amonge.g. Comparing mean blood glucose levels among the studied groups of T2diabetic patientsthe studied groups of T2diabetic patients Variable Life style group )diet +exercise( Mean +SD Oral hypoglycemic drugs Mean +SD Insulin therapy group Mean +SD ANOVA & P value Random Blood glucose (mg/dl) 135+45.5 127+42.5 118.5+25.5
  91. 91.  B- Two – way analysis of variance:B- Two – way analysis of variance: is used tois used to compare the means of more than 2 groups bycompare the means of more than 2 groups by more than one factormore than one factor e.g.e.g. )BG & cholesterol)BG & cholesterol level in 3 groups of pts: 1-lifestyle, 2-OHA,level in 3 groups of pts: 1-lifestyle, 2-OHA, 3-Insulin therapy(3-Insulin therapy(
  92. 92. e.g. Comparing mean blood glucose &e.g. Comparing mean blood glucose & cholesterol levels among the studied groups ofcholesterol levels among the studied groups of T2diabetic patientsT2diabetic patients Variable Life style group )diet +exercise( Mean +SD Oral hypoglyce mic drugs Mean +SD Insulin therapy group Mean +SD ANOVA & P value Random Blood glucose (mg/dl) 135+45.5 127+42.5 118.5+25.5 Cholester ol level 180 + 67 179 + 77.5 174 + 66.4
  93. 93. )B( Qualitative Variables 1. Chi = square test (x1. Chi = square test (x22 ):): == df= (row-1)(column-1)df= (row-1)(column-1)  O = observed valueO = observed value  E= expected value =E= expected value = == ∑ − E EO 2 )( totalgrand totalcolumnxtotalrow 2 χ
  94. 94. Association between physical activity andAssociation between physical activity and weightweight Obese- overwt Average wt Total Lack of activity 70 (E1) 30 (E2) 100 Physical activity 10 (E3) 90 (E4) 100 Total 80 120 200 N.B. Chi-square value at DF=1 equal 3.8
  95. 95. XX22‌‌‌‌‌‌‌‌ == )70-40()70-40(22 ∕40∕40 ++ )30-60()30-60(22 ∕60∕60++)10-40()10-40(22 ∕40∕40 ++ )90-60()90-60(22 ∕60=∕60= 22.5 + 15 + 22.5 +15=22.5 + 15 + 22.5 +15= 7575 calculated value > tabulated valuecalculated value > tabulated value p=0.0000p=0.0000 Obese- overwt Average wt Total Lack of activity 70 (40) 30 (60) 100 Physical activity 10 (40) 90 (60) 100 Total 80 120 200
  96. 96. Example:Example: The result of influenza vaccine trial.The result of influenza vaccine trial. InfluenzaInfluenza VaccineVaccine O EO E PlaceboPlacebo O EO E TT YesYes NoNo 6060 4040 4040 6060 100100 100100 100100 100100 200200  Expected value in every cell =Expected value in every cell = R total x C totalR total x C total = --------------------------= -------------------------- G totalG total
  97. 97.  == == ∑ − E EO 2 )(2 χ
  98. 98. (2) Z- test(2) Z- test to compare 2 proportions:to compare 2 proportions: ZZ ==  PP11= % of first group.= % of first group.  PP22=% of second group.=% of second group.  qq11= 100-p= 100-p1.1.  qq22=100-p=100-p2.2.  nn11=size of first group.=size of first group.  nn22=size of second group.=size of second group.  IfIf Z>2Z>2, the difference is statistically significance., the difference is statistically significance. 2 22 1 11 21 n qp n qp PP + −
  99. 99. Example:Example:  No of anaemic patients in group 1(50) is 5.No of anaemic patients in group 1(50) is 5.  No of anaemic patients in group 2(60) is 20.No of anaemic patients in group 2(60) is 20.  Find if gp 1 & 2 are statistically different inFind if gp 1 & 2 are statistically different in the prevalence of anaemia.the prevalence of anaemia.  We use Z test:We use Z test: PP11= 5/50 x 100= 10%.= 5/50 x 100= 10%. PP22=20/60 x 100 = 33%.=20/60 x 100 = 33%. qq11= 100-10= 90% .= 100-10= 90% . qq22=100-33= 67.=100-33= 67. nn11=50.=50. nn22=60.=60.
  100. 100. Z =Z =  Z = 3.1 > 2 so, there is statisticallyZ = 3.1 > 2 so, there is statistically significant difference between thesignificant difference between the precentages of anaemia between the 2precentages of anaemia between the 2 groups.groups. 1.34.7/23 85.3618 23 60 6733 50 9010 3310 == + = + − xx
  101. 101. Correlation & Regression  Correlation: measure the degree of associationmeasure the degree of association between 2 continuous variables.between 2 continuous variables.  Correlation is measured byCorrelation is measured by correlationcorrelation coefficientcoefficient (r)(r)..  Value of r ranged betweenValue of r ranged between +1 & -1.+1 & -1.  r=0 means no correlation.r=0 means no correlation.  r=+1 means perfect +ve association.r=+1 means perfect +ve association.  r=-1 means perfect -ve association.r=-1 means perfect -ve association.  t-testt-test for correlation is used to test thefor correlation is used to test the significance of association.significance of association.
  102. 102. Pearson correlationPearson correlation (r(r((
  103. 103. Scatter PlotsScatter Plots Strong Negative Correlation X Y r = -0.86 Strong Positive Correlation X Y r = 0.91 Positive Correlation X Y r = 0.70 No Correlation X Y r = 0.06
  104. 104. Variable Pearson correlation )r( P value MCV, fl 0.94 0.000* Platelet counts X 109 -0.42 0.061 Ferritin 0.61 0.081 Table ) (: Correlation between hemoglobin level and MCV, platelet counts, and Ferritin among the studied cases.
  105. 105. Correlation and regressionCorrelation and regression
  106. 106.  RegressionRegression gives equation for the line that bestgives equation for the line that best models the relationship between 2 variables.models the relationship between 2 variables.  Types of patternTypes of pattern:: linear, curve,linear, curve, …. Will determine…. Will determine the type of regression model to be applied to the data.the type of regression model to be applied to the data.  Linear regressionLinear regression: is the simplest form & is used: is the simplest form & is used when the relation between x & y variables iswhen the relation between x & y variables is approximated by straight line.approximated by straight line.  Linear regressionLinear regression gives thegives the equation of the straightequation of the straight line that determine the relation an prediction of aline that determine the relation an prediction of a change in a variable )dependant( due to change inchange in a variable )dependant( due to change in the other variable )independentthe other variable )independent).).
  107. 107. Linear diagramLinear diagram
  108. 108.  t-testt-test is used to assess the level ofis used to assess the level of significance.significance.  Multiple regressionMultiple regression : used to assess the: used to assess the dependency of a dependant variable ondependency of a dependant variable on several independent variables.several independent variables.  F-testF-test (ANOVA) is the test of(ANOVA) is the test of significance.significance. e.g.e.g. vit D levelvit D level ((age, amount of ca intake,age, amount of ca intake, duration of exposure to sunduration of exposure to sun, ……), ……)
  109. 109. Thank youThank you
  • rajanantwal

    Feb. 6, 2021
  • buslatan

    Apr. 12, 2019
  • shwetasingh779

    Apr. 12, 2019

DATA PRESENTATION SUMMERIZATION NORMAL DISTRIBUTION CURVE INFERENTIAL STATISTICS

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