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Lincoln Jiang Statistical Consultant Western Michigan University The Graduate College Graduate Center for Research and Retention.

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Presentation on theme: "Lincoln Jiang Statistical Consultant Western Michigan University The Graduate College Graduate Center for Research and Retention."— Presentation transcript:

1 Lincoln Jiang Statistical Consultant Western Michigan University The Graduate College Graduate Center for Research and Retention

2 Definition of Statistics Statistics is the art of making numerical conjectures about puzzling questions. --- Statistics Fourth Edition by Freedman

3 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

4 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

5 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

6 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

7 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

8 Basic Terms Variables Characteristics that can take on any number of different values Values Possible numbers or categories that of a variable can have Scores A particular person’s value on a variable

9 Types of Data Qualitative data --nonnumeric eg: types of material {straw, sticks, bricks} Quantitative -- numeric Discrete data --numeric data that have a finite number of possible values eg: counting numbers, {1,2,3,4,5} Continuous data --numeric data that have a infinite number of possible values eg: Real numbers

10 Types of Scale Nominal---have no order and thus only gives names or labels to various categories. Variables assessed on a nominal scale are called categorical variables Ordinal---have order, but the interval between measurements is not meaningful. Interval---have meaningful intervals between measurements, but there is no true starting point (zero). Eg: temperature with the Celsius scale Ratio---have the highest level of measurement. Ratios between measurements as well as intervals are meaningful because there is a starting point (zero). Eg: length, time, plane angle, energy

11 EX

12 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

13 Collecting Data “Twenty-five percent of Americans doubt that the Holocaust ever occurred.” --- a news in 1993 Census Sample Survey

14 Why Study Samples? Often not practical to study an entire population Instead, researchers attempt to make samples representative of populations Random selection Each member of population has an equal chance of being sampled Good but difficult Haphazard selection Take steps to ensure samples do not differ from the population in systematic ways Not as good but much more practical

15 Sample vs. Population Sample Relatively small number of instances that are studied in order to make inferences about a larger group from which they were drawn Population The larger group from which a sample is drawn

16 Sample vs. Population Examples Population a. pot of beans b. larger circle c. histogram Sample a. spoonful b. smaller circle c. shaded scores

17 Sampling Methods Simple Random Sampling Systematic sampling Stratified sampling Cluster sampling Other samplings: Quota sampling, Mechanical sampling and so on

18 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

19 After Collecting…….Before Analyzing….

20 Frequency Tables Frequency table Shows how many times each value was used for a particular variable Percentage of scores of each value Grouped frequency table Range of scores in each of several equally sized intervals

21 Steps for Making a Frequency Table 1. Make a list of each possible value, from highest to lowest 2. Go one by one through the data, making a mark for each data next to its value on the list 3. Make a table showing how many times each value on your list was used 4. Figure the percentage of data for each value

22 A Frequency Table Stress ratingFrequencyPercent,% 10149.3 9159.9 82617.2 73120.5 6138.6 51811.9 41610.6 3127.9 232.0 110.7 021.3

23 A Grouped Frequency Table Stress rating intervalFrequencyPercent 10-11149 8-94127 6-74429 4-53423 2-31510 0-132

24 Frequency Graphs Histogram Depicts information from a frequency table or a grouped frequency table as a bar graph EX2

25 Shapes of Frequency Distributions Unimodal Having one peak Bimodal Having two peaks Multimodal Having two or more peaks Rectangular Having no peaks

26 Symmetrical vs. Skewed Frequency Distributions Symmetrical distribution Approximately equal numbers of observations above and below the middle Skewed distribution One side is more spread out that the other, like a tail Direction of the skew Right or left (i.e., positive or negative) Side with the fewer scores Side that looks like a tail

27 Skewed Frequency Distributions Skewed right (b) Fewer scores right of the peak Positively skewed Can be caused by a floor effect Skewed left (c) Fewer scores left of the peak Negatively skewed Can be caused by a ceiling effect

28 Ceiling and Floor Effects Ceiling effects Occur when scores can go no higher than an upper limit and “pile up” at the top e.g., scores on an easy exam, as shown on the right Causes negative skew Floor effects Occur when scores can go no lower than a lower limit and pile up at the bottom e.g., household income Causes positive skew

29 Kurtosis Degree to which tails of the distribution are “heavy” or “light” heavy tails = higher Kurtosis(b) Light tails = lower Kurtosis(c) Normal distribution= Zero Kurtosis (a)

30 Measures of Central Tendency Central tendency = representative or typical value in a distribution mean, the median and the mode can measure central tendency. Mean Computed by Summing all the scores (sigma,  ) Dividing by the number of scores (N)

31 Measures of Central Tendency Mean Often the best measure of central tendency Most frequently reported in research articles Think of the mean as the “balancing point” of the distribution

32 Measures of Central Tendency Mode Most common single number in a distribution If distribution is symmetrical and unimodal, the mode = the mean Typical way of describing central tendency of a nominal variable

33 Measures of Central Tendency Median Middle value in a group of scores Point at which half the scores are above half the scores are below Unaffected by extremity of individual scores Unlike the mean Preferable as a measure of central tendency when a distribution has some extreme scores

34 Measures of Central Tendency Examples of means as balancing points of various distributions Does not have to be a score exactly at the median Note that a score’s distance from the balancing point matters in addition to the number of scores above or below it

35 Measures of Central Tendency Examples of means and modes

36 Measures of Central Tendency Steps to computing the median 1. Line up scores from highest to lowest 2. Figure out how many scores to the middle Add 1 to number of scores Divide by 2 3. Count up to middle score If there is 1 middle score, that’s the median If there are 2 middle scores, median is their average Ex3

37 Measures of Variation Variation = how spread out data is Variance Measure of variation Average of each score’s squared deviations (differences) from the mean

38 Measures of Variation Steps to computing the variance 1. Subtract the mean from each data 2. Square each deviation value 3. Add up the squared deviation scores 4. Divide sum by the number of scores Ex4

39 Measures of Variation Standard deviation Another measure of variation, roughly the average amount that scores differ from the mean Used more widely than variance Abbreviated as “SD” To compute standard deviation Compute variance Simply take the square root SD is square root of variance Variance is SD squared

40 Two Branches of Statistical Methods Descriptive statistics Summarize and describe a group of numbers such as the results of a research study Inferential statistics Allow researchers to draw conclusions and inferences that are based on the numbers from a research study, but go beyond these numbers

41 The Normal Curve Often seen in social and behavioral science research and in nature generally Particular characteristics Bell-shaped Unimodal Symmetrical Average tails Bean Machine

42 Z Scores indicates how many standard deviations an observation is above or below the mean If Z>0, indicate the data > mean If Z<0, indicate the data < mean Z score of 1.0 is one SD above the mean Z score of -2.5 is two-and-a-half SDs below the mean Z score of 0 is at the mean

43 Z Scores When values in a distribution are converted to Z scores, the distribution will have Mean of 0 Standard deviation of 1 Useful Allows variables to be compared to one another Provides a generalized standard of comparison

44 Z Scores To compute a Z score, subtract the mean from a raw score and divide by the SD To convert a Z score back to a raw score, multiply the Z score by the SD and then add the mean Ex5

45 Confidence Interval confidence interval (CI) is a particular kind of interval estimate of a population parameter. How likely the interval is to contain the parameter is determined by the confidence level "95% confidence interval" Animation ex6

46 Correlation A statistic for describing the relationship between two variables Examples Price of a bottle of wine and its quality Hours of studying and grades on a statistics exam Income and happiness Caffeine intake and alertness

47 Graphing Correlations on a Scatter Diagram Scatter diagram Graph that shows the degree and pattern of the relationship between two variables Horizontal axis Usually the variable that does the predicting e.g., price, studying, income, caffeine intake Vertical axis Usually the variable that is predicted e.g., quality, grades, happiness, alertness

48 Graphing Correlations on a Scatter Diagram Steps for making a scatter diagram 1. Draw axes and assign variables to them 2. Determine the range of values for each variable and mark the axes 3. Mark a dot for each person’s pair of scores

49 Correlation Linear correlation Pattern on a scatter diagram is a straight line Example above Curvilinear correlation More complex relationship between variables Pattern in a scatter diagram is not a straight line Example below

50 Correlation Positive linear correlation High scores on one variable matched by high scores on another Line slants up to the right Negative linear correlation High scores on one variable matched by low scores on another Line slants down to the right

51 Correlation Zero correlation No line, straight or otherwise, can be fit to the relationship between the two variables Two variables are said to be “uncorrelated”

52 Correlation Review a. Negative linear correlation b. Curvilinear correlation c. Positive linear correlation d. No correlation

53 Correlation Coefficient Correlation coefficient, r, indicates the precise degree of linear correlation between two variables Computed by taking “cross-products” of Z scores Multiply Z score on one variable by Z score on the other variable Compute average of the resulting products Can vary from -1 (perfect negative correlation) through 0 (no correlation) to +1 (perfect positive correlation)

54 Linear Correlation Examples

55 Correlation and Causality When two variables are correlated, three possible directions of causality X->Y X<-Y X Y Inherent ambiguity in correlations Knowing that two variables are correlated tells you nothing about their causal relationship

56 Prediction Correlations can be used to make predictions about scores Predictor X variable Variable being predicted from Criterion Y variable Variable being predicted Sometimes called “regression”

57 Multiple Correlation and Multiple Regression Multiple correlation Association between criterion variables and two or more predictor variables Multiple regression Making predictions about criterion variables based on two or more predictor variables Unlike prediction from one variable, standardized regression coefficient is not the same as the ordinary correlation coefficient

58 Proportion of Variance Accounted For Correlation coefficients Indicate strength of a linear relationships Cannot be compared directly e.g., an r of.40 is more than twice as strong as an r of.20 To compare correlation coefficients, square them An r of.40 yields an r 2 of.16; an r of.20 an r 2 of.04 Squared correlation indicates the proportion of variance on the criterion variable accounted for by the predictor variable R-square

59 Most Commonly Used Statistical Techniques Linear Regression (Predicts the value of one numerical variable given another variable) - How much does the maximum legibility distance of Highway signs decrease when age is increased?

60 Data on winning bid price for 12 Saturn cars on eBaY in July 2002 Simple linear regression is a data analysis technique that tries to find a linear pattern in the data. In linear regression, we use all of the data to calculate a straight line which may be used to predict Price based on Miles. Since Miles is used to predict Price, Miles is called an `Explanatory (Independent) Variable' while Price is called a `Response (Dependent) Variable'.

61 The slope of the line is -.05127, which means that predicted Price tends to drop 5 cents for every additional mile driven, or about $512.70 for every 10,000 miles. The intercept (or Y-intercept) of the line is $8136; this should not be interpreted as the predicted price of a car with 0 mileage because the data provides information only for Saturn cars between 9,300 miles and 153,260 miles We can now use the line to predict the selling price of a car with 60000 miles. What is the height or Y value of the line at X=60000? The answer is

62 Most Commonly Used Statistical Techniques T-test (for the means) - What is the mean time that college students watch TV per day? - What is the mean pulse rate of women?

63 Hypothesis Testing Procedure for deciding whether the outcome of a study supports a particular theory or practical innovation

64 Core Logic of Hypothesis Testing Approach can seem curious or even backwards Researcher considers the probability that the experimental procedure had no effect and that the observed result could have occurred by chance alone If that probability is sufficiently low, researcher will… Reject the notion that experimental procedure had no effect Affirm the hypothesis that the procedure did have an effect

65 The Null Hypothesis and the Research Hypothesis Null hypothesis (H 0 ) Opposite of desired result Usually that manipulation had no effect Research hypothesis (H 1 ) Also called the “alternative hypothesis” Opposite of the null hypothesis What the experimenter desired or expected all along— that the manipulation did have an effect

66 One-tailed vs. Two-tailed Hypothesis Tests Directional prediction Researcher expects experimental procedure to have an effect in a particular direction One-tailed significance tests may be used Nondirectional prediction Research expects experimental procedure to have an effect but does not predict a particular direction Two-tailed significance test appropriate Takes into account that the sample could be extreme at either tail of the comparison distribution

67 One-tailed vs. Two-tailed Hypothesis Tests Two-tailed tests More conservative than one-tailed tests Some believe that two-tailed tests should always be used, even when an experimenter makes a directional prediction

68 Significance Level Cutoffs for One- and Two-Tailed Tests The.05 significance level The.01 significance level

69 Decision Errors When the right procedure leads to the wrong conclusion Type I Error Reject the null hypothesis when it is true Conclude that a manipulation had an effect when in fact it did not Type II Error Fail to reject the null when it is false Conclude that a manipulation did not have an effect when in fact it did

70 P-value is the probability of obtaining a result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. Frequent misunderstandings For more details, please refer to Wikipedia.

71 Decision Errors Setting a strict significance level (e.g., p <.001) Decreases the possibility of committing a Type I error Increases the possibility of committing a Type II error Setting a lenient significance level (e.g., p <.10) Increases the possibility of committing a Type I error Decreases the possibility of committing a Type II error

72 Test Statistic  value computed from sample information  Basis for rejecting/ not rejecting the null hypothesis  used to compute the p-value Example:

73 T-test A t-test is most commonly applied when the test statistic would follow a normal distribution. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic follows a Student's t distribution.

74 t-test One-sample t test Two-sample t test Independent two-sample Dependent two-sample Equal sample size, equal variance Unequal sample size, equal variance

75 The Hypothesis Testing Process 1. Restate the research question as a research hypothesis and a null hypothesis about the populations 2. Set the level of significance, . 3. Collect the sample and compute for the test statistic. 4. Assume Ho is true, compute the p-value. 5. If p-value < , reject Ho. 6. State your conclusion. SUMMARY OF HYPOTHESIS TESTS Ex7,8

76 Most Commonly Used Statistical Techniques Analysis of Variance (testing differences of means for 2 or more groups) - Is GPA related to where a student likes to sit (front, middle, back)? - Which internet search engine is the fastest?

77 Analysis of Variance Abbreviated as “ANOVA” Used to compare the means of more than two groups Null hypothesis is that all populations being studied have the same mean Reject null if at least one population has a mean that differs from the others Actually works by analyzing variances

78 Two Different Ways of Estimating Population Variance Estimate population variance from variation within each group Is not affected by whether or not null hypothesis is true Estimate population variance from variation between each group Is affected by whether or not null hypothesis is true

79 Two Important Questions 1. How to estimate population variation from variance between groups? 2. How is that estimate affected by whether or not the null is true?

80 Estimate population variance from variation between means of groups First, variation among means of samples is related directly to the amount of variation within each population from which samples are taken The more variation within each population, the more variation in means of samples taken from those populations Note that populations on the right produce means that are more scattered

81 Estimate population variance from variation between means of groups And second, when null is false there is an additional source of variation When null hypothesis is true (left), variation among means of samples caused by Variation within the populations When null hypothesis is false (right), variation among means of samples caused by Variation within the populations And also by variation among the population means

82 Basic Logic of ANOVA ANOVA entails a comparison between two estimates of population variance Ratio of between-groups estimate to within-groups estimate called an F ratio Compare obtained F value to an F distribution

83 Assumptions of an ANOVA Populations follow a normal curve Populations have equal variances As for t tests, ANOVAs often work fairly well even when those assumptions are violated

84 Rejecting the Null Hypothesis A significant F tells you that at least one of the means differs from the others Does not indicate how many differ Does not indicate which one(s) differ For more specific conclusions, a researcher must conduct follow-up t tests Problem: Lots of t tests increases the chances of finding a significant result just by chance (i.e., increases chances beyond p =.05)

85 ANOVA (continue) Procedure that allows one to examine two or more variables in the same study Efficient Allows for examination of interaction effects An ANOVA with only one variable is a one-way ANOVA, an ANOVA with two variables is a two-way ANOVA, and so on

86 Main Effects vs. Interactions A main effect refers to the effect of one variable, averaging across the other(s) An interaction effect refers to a case in which the effect of one variable depends on the level of another variable

87 Main Effects vs. Interactions

88 Most Commonly Used Statistical Techniques Chi-square test of independence (Relationship of 2 categorical variables) -With whom is it easier to make friends with? - Does the opinion on legalization of marijuana depend on one’s religion?

89 Chi-Square Tests Hypothesis testing procedure for nominal variables Focus on number of people/items in each category (e.g., hair color, political party, gender) Compare how well an observed distribution fits an expected distribution Expected distribution can be based on A theory Prior results Assumption of equal distribution across categories

90 Chi-Square Test for Goodness of Fit Single nominal variable Degrees of freedom = number of categories minus 1

91 Chi-Square Statistic Compares observed frequency distribution to expected frequency distribution Compute difference between observed and expected and square each one Weight each by its expected frequency Sum them Ex9

92 Chi-Square Distribution Compare obtained chi-square to a chi-square distribution Does mismatch between observed and expected frequency exceed what would be expected by chance alone?

93 Chi-Square Test for Independence Two nominal variables Independence means no relation between variables To determine degrees of freedom… Contingency table Lists number of observations for each combination of categories To determine expected frequencies…

94 Most Commonly Used Statistical Techniques Correlation (Relationship of 2 numerical variables) - Is there a connection between the average verbal SAT and the percent of graduates who took the SAT in a state?

95 Other Statistical Techniques Factor analysis (reducing independent variables which are highly correlated) Cluster analysis (grouping observations with similar characteristics) Correspondence Analysis (grouping the levels of 2 or more categorical variables) Time Series Analysis And so on……..

96 Inference with highest confidence level

97 Definition of Statistics Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. ---From Wikipedia

98 Presentation of Data FOR CATEGORICAL DATA --- Bar Chart --- Pie Chart

99 Presentation of Data FOR NUMERICAL DATA --- Stem-and-Leaf Plot --- Histogram --- Boxplot

100 Overview of Statistical Techniques

101

102 Questions? or Comments ?

103 Upcoming Workshops 10/26/2009 Overview of SPSS 12/02/2009 Overview of SAS

104 How to lie with statistics 1.The Sample with Built-in Bias. 2.Well-Chosen Average. 3.The Gee-Whiz Graph. 4.Correlation and Causation.


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