invest_3ed.pdf

# Use not equal to alternative for confidence interval

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Use “not equal to” alternative for confidence interval Hypergeometric probability Graph > Probability Distribution Plot Generate data from hypergeometric Calc > Random Data > Hypergeometric Create Boolean for values below some number x (creating an empirical p-value) Calc > Calculator, expression: sum(c7 <= x)/n MTB> let c10 = (c7 <= x) MTB> let k1 = sum(10)/n MTB> print k1 Fisher’s Exact Test Stat > Basic Statistics > 2 Proportions

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Chance/Rossman, 2015 ISCAM III Chapter 4 254 CHAPTER 4: COMPARISONS WITH QUANTITATIVE VARIABLES This chapter parallels the previous one in many ways. We will continue to consider studies where the goal is to compare a response variable between two groups. The difference here is that these studies will involve a quantitative response variable rather than a categorical one. The methods that we employ to analyze these data will therefore be different, but you will find that the basic concepts and principles that you learned in Chapters 1 ± 3 still apply. These include the principle of starting with numerical and graphical summaries to explore the data, the concept of statistical significance in determining whether the difference in the distribution of the response variable between the two groups is larger than we would reasonably expect from randomness alone, and the importance of considering how the data were collected in determining the scope of conclusions that can be drawn from the study. Section 1: Comparing groups Quantitative response Investigation 4.1: Employment discrimination? Section 2: Comparing two population means Investigation 4.2: NBA Salaries Independent random samples, t procedures Investigation 4.3: Left-handedness and life expectancy Factors influencing significance Section 3: Comparing for two treatment means Investigation 4.4: Lingering effects of sleep deprivation Randomization tests Investigation 4.5: Lingering effects of sleep deprivation (cont.) Two-sample t -tests Investigation 4.6: Ice cream serving sizes Two-sample t -confidence interval Investigation 4.7: Cloud seeding Strategies for non-normal data Section 4: Matched Pairs Designs Investigation 4.8: Chip melting times Independent vs. paired design, technology Investigation 4.9: Chip melting times (cont.) Inference (simulation, paired t -test) Investigation 4.10: Comparison shopping Application Investigation 4.11: Smoke alarms McNemar’s test (paired categorical data) Example 4.1: Age Discrimination? Randomization test Example 4.2: Speed Limit Changes Two-sample t -procedures Example 4.3: Distracted Driving? (cont.) Paired t -procedures Investigating Statistical Concepts, Applications, and Methods
Chance/Rossman, 2015 ISCAM III Investigation 4.1 255 SECTION 1: COMPARING GROUPS QUANTITATIVE REPONSE In this chapter, we will focus on comparing two groups on a quantitative response variable. Again, the reasoning is still the same as in Ch. 1 3, but we will see some small changes in the details. We start with a small

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