Chapter 3
... sample values selected from one group are not related to or somehow paired or matched with the sample values from the other groups. Two groups can be dependent if the sample values are paired. (That is, each pair of sample values consists of two measurements from the same subject (such as before/aft ...
... sample values selected from one group are not related to or somehow paired or matched with the sample values from the other groups. Two groups can be dependent if the sample values are paired. (That is, each pair of sample values consists of two measurements from the same subject (such as before/aft ...
Transformations in Single Factor Experiments
... Which assumption required for one-way ANOVA appears to be violated here? Clearly the variation in the peak discharge differs for the estimation methods. This supported by using either Barlett’s and Levene’s Test for the equality of the population variances, which are shown below. (see pgs. 80-81) ...
... Which assumption required for one-way ANOVA appears to be violated here? Clearly the variation in the peak discharge differs for the estimation methods. This supported by using either Barlett’s and Levene’s Test for the equality of the population variances, which are shown below. (see pgs. 80-81) ...
Choosing the Appropriate Statistics
... Basic premise: given previous observations, one can (within some range of error) predict what is likely to occur in future Examples: GRE scores and performance in grad school Parental smoking and smoking in teens Cholesterol level and risk for heart attack. Maternal depression and childhood ...
... Basic premise: given previous observations, one can (within some range of error) predict what is likely to occur in future Examples: GRE scores and performance in grad school Parental smoking and smoking in teens Cholesterol level and risk for heart attack. Maternal depression and childhood ...
Chapter 3 Experiments with a Single Factor: The Analysis
... • Completely randomized design: the experiments are performed in random order so that the environment in which the treatment are applied is as uniform as possible. • For hypothesis testing, the model errors are assumed to be normally and independently distributed random variables with mean zero and ...
... • Completely randomized design: the experiments are performed in random order so that the environment in which the treatment are applied is as uniform as possible. • For hypothesis testing, the model errors are assumed to be normally and independently distributed random variables with mean zero and ...
power point file
... A large value of F indicates relatively more difference between groups than within groups (evidence against H0) To get the P-value, we compare to F(I-1,n-I)-distribution • I-1 degrees of freedom in numerator (# groups -1) • n - I degrees of freedom in denominator (rest of df) ...
... A large value of F indicates relatively more difference between groups than within groups (evidence against H0) To get the P-value, we compare to F(I-1,n-I)-distribution • I-1 degrees of freedom in numerator (# groups -1) • n - I degrees of freedom in denominator (rest of df) ...
ANOVAs01
... A large value of F indicates relatively more difference between groups than within groups (evidence against H0) To get the P-value, we compare to F(I-1,n-I)-distribution • I-1 degrees of freedom in numerator (# groups -1) • n - I degrees of freedom in denominator (rest of df) ...
... A large value of F indicates relatively more difference between groups than within groups (evidence against H0) To get the P-value, we compare to F(I-1,n-I)-distribution • I-1 degrees of freedom in numerator (# groups -1) • n - I degrees of freedom in denominator (rest of df) ...
Inferential Statistics Probability From Samples to Populations
... • A type I error is made when a researcher rejects the null hypothesis when it is true • The probability of making this type of error is equal to the level of significance • A type II error is made when a researcher accepts the null hypothesis when it is false • As the level of significance incr ...
... • A type I error is made when a researcher rejects the null hypothesis when it is true • The probability of making this type of error is equal to the level of significance • A type II error is made when a researcher accepts the null hypothesis when it is false • As the level of significance incr ...
Experimental Design
... That is, we seek to eliminate the effect of extraneous factors within a block so that the between treatment effect (our main concern) can be more precisely measured. • Example: Suppose we wish to investigate the differences in raw materials from three different vendors. Processing will take place on ...
... That is, we seek to eliminate the effect of extraneous factors within a block so that the between treatment effect (our main concern) can be more precisely measured. • Example: Suppose we wish to investigate the differences in raw materials from three different vendors. Processing will take place on ...
12: Analysis of Variance Introduction
... The name analysis of variance may mislead some students to think the technique is used to compare group variances. In fact, analysis of variance uses variance to cast inference on group means. The null and alternative hypotheses are: H 0 : :1 = :2 = . . . = :k H 1: H 0 is false (“at least one popula ...
... The name analysis of variance may mislead some students to think the technique is used to compare group variances. In fact, analysis of variance uses variance to cast inference on group means. The null and alternative hypotheses are: H 0 : :1 = :2 = . . . = :k H 1: H 0 is false (“at least one popula ...
One-Way Analysis of Variance (ANOVA) Example Problem
... is not statistically equal for compact, midsize, and full size cars. However, since only one mean must be different to reject the null, we do not yet know which mean(s) is/are different. In short, an ANOVA test will test us that at least one mean is different, but an additional test must be conducte ...
... is not statistically equal for compact, midsize, and full size cars. However, since only one mean must be different to reject the null, we do not yet know which mean(s) is/are different. In short, an ANOVA test will test us that at least one mean is different, but an additional test must be conducte ...
Lecture 19 - Wharton Statistics
... •A composite score called the Safety Climate Index was calculated. Its values are between 0-100. •The workers were classified according to their job category as unskilled, skilled and supervisor. ...
... •A composite score called the Safety Climate Index was calculated. Its values are between 0-100. •The workers were classified according to their job category as unskilled, skilled and supervisor. ...
Six Sigma Black Belt Training
... observations from their means. Consider the following example with two groups. The measurements show the thumb lengths in centimeters of two types of primates. ...
... observations from their means. Consider the following example with two groups. The measurements show the thumb lengths in centimeters of two types of primates. ...
Statistical methods for comparing multiple groups
... We assume the variance σ 2 is the same for each of the group’s populations We can pool (combine) the estimates of σ 2 across groups and use an overall estimate for the common population variance: ...
... We assume the variance σ 2 is the same for each of the group’s populations We can pool (combine) the estimates of σ 2 across groups and use an overall estimate for the common population variance: ...
Chapter 11
... variances for the two samples Estimated standard error uses calculated pooled variance ...
... variances for the two samples Estimated standard error uses calculated pooled variance ...
Analysis of variance
Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences among group means and their associated procedures (such as ""variation"" among and between groups), developed by statistician and evolutionary biologist Ronald Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the t-test to more than two groups. As doing multiple two-sample t-tests would result in an increased chance of committing a statistical type I error, ANOVAs are useful for comparing (testing) three or more means (groups or variables) for statistical significance.