Chapter 14
Section 14.1: Linear Equations w/ One Independent Variable
Slopes: b1 > 0 positive slope; b1 < 0 negative slope; b1 = 0 horizontal slope
The number b1 measures the steepness of the line; mo
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9.6 Wilcoxon Signed Rank Test (TABLE
V)
Use when you dont know if means are
normal/know they are abnormal,
distribution NOT R/L skewed
Nonparametric: inferential methods not
concerned with parameters
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Inferences for
Two Population Means
Estimating two population values
Paired Samples
Population means, independent samples
Population Portions
The point of estimate for difference is
X1 bar X2 bar
Diff
Confidence Intervals
Confidence interval estimate for the mean of y given a particular xp
Size of interval varies according to distance away from mean, x
Confidence interval estimate for an individual
Descriptive Methods in Regression and Correlation
Scatter Plots and Correlation
A scatter plot (or scatter diagram) is used to show the relationship between two
variables
Correlation analysis is used
Chi-Square and F-Distribution
The chi-square distribution is a family of distributions, depending on degrees of
freedom:
d.f. = n - 1
The critical value, X^2 , is found from the chi-square table
F-Dis
Correlation and Outlier
Correlation Coefficient
Correlation measures the strength of the linear association between two variables
The sample correlation coefficient r is a measure of the strength of t
Estimating Single
Population Parameters
Confidence Interval
Confidence Intervals for the Population Mean,
when Population Standard Deviation is Known
when Population Standard Deviation is Unknown
Det
Hypothesis Tests and Estimation
for Population Variances
Hypothesis Tests for Variances
Test for a single population variance
Test for two population variances
Chi-square test statistic
F-statistic
A
Influential Cases
An influential case is one that has both high leverage and a large Studentized
residual, thus enabling it to substantially change the regression model all by itself.
To check if a su
Inferential Methods in
Regression and Correlation
Standard Error of Estimates
The standard deviation of the variation of observations around the simple
regression line is estimated by
SSE = Sum of squ