The following table summarizes the results of a study on SAT prep courses, comparing SAT scores of students in a private preparation class, a high school preparation class, and no preparation class. Use the information from the table to answer the remaining questions. Treatment Private prep class High school prep class No prep class Number of Observations 60 60 Source Between treatments Within treatments 60 Sample Mean 650 645 625 Using the data provided, complete the partial ANOVA summary table that follows. (Hint: T, the treatment total, can be calculated as the sample mean times the number of observations. G, the grand total, can be calculated from the values of T once you have calculated them.) Sum of Squares (SS) 132,750.00 147,500.00 Sum of Squares (SS) df Mean Square (MS) 162,250.00 In some ANOVA summary tables you will see, the labels in the first (source) column are Treatment, Error, and Total. Which of the following reasons best explains why the within-treatments variance is sometimes referred to as the "error variance"? The within-treatments variance measures random, unsystematic differences within each of the samples assigned to each of the treatments. These differences are not due to treatment effects because everyone within each sample received the same treatment; therefore, the differences are sometimes referred to as "error." The within-treatments variance measures treatment effects as well as random, unsystematic differences within each of the samples assigned to each of the treatments. These differences represent all of the variations that could occur in a study; therefore, they are sometimes referred to as "error." Differences among members of the sample who received the same treatment occur when the researcher makes an error, and thus these differences are sometimes referred to as "error." In ANOVA, the F test statistic is the within-treatments variance. The value of the F test statistic is When the null hypothesis is true, the F test statistic is false, the F test statistic is most likely hypothesis for Differences among members of the sample who received the same treatment occur because some treatments are more effective than others, so it would be an error to receive the less superior treatments. of the between-treatments variance and the . When the null hypothesis is . In general, you should reject the null
The following table summarizes the results of a study on SAT prep courses, comparing SAT scores of students in a private preparation class, a high school preparation class, and no preparation class. Use the information from the table to answer the remaining questions. Treatment Private prep class High school prep class No prep class Number of Observations 60 60 Source Between treatments Within treatments 60 Sample Mean 650 645 625 Using the data provided, complete the partial ANOVA summary table that follows. (Hint: T, the treatment total, can be calculated as the sample mean times the number of observations. G, the grand total, can be calculated from the values of T once you have calculated them.) Sum of Squares (SS) 132,750.00 147,500.00 Sum of Squares (SS) df Mean Square (MS) 162,250.00 In some ANOVA summary tables you will see, the labels in the first (source) column are Treatment, Error, and Total. Which of the following reasons best explains why the within-treatments variance is sometimes referred to as the "error variance"? The within-treatments variance measures random, unsystematic differences within each of the samples assigned to each of the treatments. These differences are not due to treatment effects because everyone within each sample received the same treatment; therefore, the differences are sometimes referred to as "error." The within-treatments variance measures treatment effects as well as random, unsystematic differences within each of the samples assigned to each of the treatments. These differences represent all of the variations that could occur in a study; therefore, they are sometimes referred to as "error." Differences among members of the sample who received the same treatment occur when the researcher makes an error, and thus these differences are sometimes referred to as "error." In ANOVA, the F test statistic is the within-treatments variance. The value of the F test statistic is When the null hypothesis is true, the F test statistic is false, the F test statistic is most likely hypothesis for Differences among members of the sample who received the same treatment occur because some treatments are more effective than others, so it would be an error to receive the less superior treatments. of the between-treatments variance and the . When the null hypothesis is . In general, you should reject the null
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Probelm #6

Transcribed Image Text:The following table summarizes the results of a study on SAT prep courses, comparing SAT scores
of students in a private preparation class, a high school preparation class, and no preparation
class. Use the information from the table to answer the remaining questions.
Treatment
Private prep class
High school prep
class
No prep class
Number of
Observations
60
60
Source
Between treatments
Within treatments
60
Sample
Mean
650
645
625
Using the data provided, complete the partial ANOVA summary table that follows. (Hint: T, the
treatment total, can be calculated as the sample mean times the number of observations. G, the
grand total, can be calculated from the values of T once you have calculated them.)
Sum of Squares
(SS)
132,750.00
147,500.00
Sum of Squares (SS) df Mean Square (MS)
162,250.00
In some ANOVA summary tables you will see, the labels in the first (source) column are Treatment,
Error, and Total. Which of the following reasons best explains why the within-treatments variance is
sometimes referred to as the "error variance"?
O The within-treatments variance measures random, unsystematic differences within each
of the samples assigned to each of the treatments. These differences are not due to
treatment effects because everyone within each sample received the same treatment;
therefore, the differences are sometimes referred to as "error."
O The within-treatments variance measures treatment effects as well as random,
unsystematic differences within each of the samples assigned to each of the treatments.
These differences represent all of the variations that could occur in a study; therefore,
they are sometimes referred to as "error."
When the null hypothesis is true, the F test statistic is
false, the F test statistic is most likely
hypothesis for
Differences among members of the sample who received the same treatment occur when
the researcher makes an error, and thus these differences are sometimes referred to as
"error."
In ANOVA, the F test statistic is the
within-treatments variance. The value of the F test statistic is
O Differences among members of the sample who received the same treatment occur
because some treatments are more effective than others, so it would be an error to
receive the less superior treatments.
of the between-treatments variance and the
When the null hypothesis is
. In general, you should reject the null
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Problem #6
apart of the question above

Transcribed Image Text:When the null hypothesis is true, the F test statistic is
▼
false, the F test statistic is most likely
hypothesis for
.
When the null hypothesis is
. In general, you should reject the null
Solution
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