invest_3ed.pdf

# Techniques apply whether the data have been collected

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techniques apply whether the data have been collected as independent random samples or from a randomized experiment, although this data collection distinction strongly influences the scope of conclusions that you can draw from the study. You will see a similar pattern in this chapter as you extend your analyses to exploring two or more groups. In particular, you will study a procedure for comparing a categorical response variable across several groups and a procedure for comparing a quantitative response variable across several groups. You will also study the important notion of association between variables, first with categorical variables and then for studies in which both variables are quantitative. In this latter case, you will also learn a new set of numerical and graphical summaries for describing these relationships. Section 1: Two Categorical Variables Investigation 5.1: Dr. Spock’s t rial Chi-square test for homogeneity of proportions Investigation 5.2: Nightlights and near-sightedness (cont.) Chi-square test for association Technology Exploration: Randomization test for chi-square statistic Investigation 5.3: Newspaper credibility decline Comparing distributions Section 2: Comparing Several Population Means Investigation 5.4: Disability discrimination Reasoning of ANOVA Applet Exploration: Randomization test for ANOVA Investigation 5.5: Restaurant spending and music ANOVA practice Applet Exploration: Exploring ANOVA Section 3: Two Quantitative Variables Investigation 5.6: Cat jumping Scatterplots Investigation 5.7: Drive for show, putt for dough Correlation coefficients Applet Exploration: Correlation guessing game Investigation 5.8: Height and foot size Least squares regression Applet Exploration: Behavior of regression lines Resistance Excel Exploration: Minimization criteria Investigation 5.9: Money-making movies Application Section 4: Inference for Regression Investigation 5.10: Running out of time Inference for regression (sampling) Investigation 5.11: Running out of time (cont.) Inference for regression (shuffling) Investigation 5.12: B oys’ heights – Regression model Investigation 5.13: Cat jumping (cont.) Confidence intervals for regression Investigation 5.14: Housing prices Transformations Technology Exploration: The regression effect Example 5.1: Internet Use by Region Example 5.2: Lifetimes of Notables Example 5.3: Physical Education Class Performance Example 5.4: Comparing Popular Diets Investigating Statistical Concepts, Applications, and Methods

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Chance/Rossman, 2015 ISCAM III Investigation 5.1 320 SECTION 1: TWO CATEGORICAL VARIABLES In Chapter 3 you learned inference procedures for assessing whether two population proportions are equal and whether two categorical variables are independent. Those methods were limited to binary variables. In this section, you will expand on your earlier techniques to allow for more than two categories in each categorical variable.
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