In a study conducted to investigate browsing activity by shoppers, each shopper was initially classified as a nonbrowser, light browser, or heavy browser. For each shopper, the study obtained a measure to determine how comfortable the shopper was in a store. Higher scores indicated greater comfort. Suppose the following data were collected. Light Heavy Nonbrowser Browser Browser 6 5 9 7 6 11 8 5 9 5 4 11 5 7 8 6 4 10 7 6 9 6 5 11 a. Use to test for a difference among mean comfort scores for the three types of browsers. Compute the values identified below (to 2 decimals, if necessary). Sum of Squares, Treatment Sum of Squares, Error Mean Squares, Treatment Mean Squares, Error Calculate the value of the test statistic (to 2 decimals, if necessary). The p-value is: less than .01 between .01 and .025 between .025 and .05 between .05 and .10 greater than .10 What is your conclusion? Conclude that the mean comfort scores are not all the same for the browser groups Do not reject the assumption that the mean comfort scores are equal for the browser groups b. Use Fisher's LSD procedure to compare the comfort levels of nonbrowsers and light browsers. Use .05 Compute the LSD critical value (to 2 decimals). What is your conclusion? Conclude that nonbrowsers and light browsers have different mean comfort scores Cannot conclude that nonbrowsers and light browsers have different mean comfort scores

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Description: In a study conducted to investigate browsing activity by shoppers, each shopper was initially classified as a nonbrowser, light browser, or heavy browser. For each shopper, the study obtained a measure to determine how comfortable the shopper was in

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

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In a study conducted to investigate browsing activity by shoppers, each shopper was initially classified as a nonbrowser, light browser, or heavy browser. For each shopper, the study obtained a measure to determine how comfortable the shopper was in a store. Higher scores indicated greater comfort. Suppose the following data were collected.

	Light	Heavy
Nonbrowser	Browser	Browser
6	5	9
7	6	11
8	5	9
5	4	11
5	7	8
6	4	10
7	6	9
6	5	11

a. Use to test for a difference among mean comfort scores for the three types of browsers.

Compute the values identified below (to 2 decimals, if necessary).

Sum of Squares, Treatment
Sum of Squares, Error
Mean Squares, Treatment
Mean Squares, Error

Calculate the value of the test statistic (to 2 decimals, if necessary).

The p-value is:

less than .01

between .01 and .025

between .025 and .05

between .05 and .10

greater than .10

What is your conclusion?

Conclude that the mean comfort scores are not all the same for the browser groups

Do not reject the assumption that the mean comfort scores are equal for the browser groups

b. Use Fisher's LSD procedure to compare the comfort levels of nonbrowsers and light browsers. Use .05

Compute the LSD critical value (to 2 decimals).

What is your conclusion?

Conclude that nonbrowsers and light browsers have different mean comfort scores

Cannot conclude that nonbrowsers and light browsers have different mean comfort scores

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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