Question

(1 point) A professor using an open source introductory statistics book predicts that 15% of the...

(1 point) A professor using an open source introductory statistics book predicts that 15% of the students will purchase a hard copy of the book, 40% will print it out from the web, and 45% will read it online. At the end of the semester he asks his students to complete a survey where they indicate what format of the book they used. Of the 152 students, 20 said they bought a hard copy of the book, 68 said they printed it out from the web, and 64 said they read it online. Round all answers to four decimal places.

1. State the hypotheses for testing if the professor's predictions were inaccurate.

?0H0: The professor's predictions  ? were were not  accurate. The difference  ? is is not  due to chance.

??HA: The professor's predictions  ? were were not  accurate. The difference  ? is is not  due to chance.

2. Enter the expected values for the hypothesis test in the table below:

Book Format Expected Value
Hard Copy
Print from Web
Read Online

3. Calculate the chi-squared statistic:

4. Calculate the degrees of freedom:

5. Calculate the p-value:

6. Based on the p-value, we have:
A. little evidence
B. very strong evidence
C. extremely strong evidence
D. strong evidence
E. some evidence
that the null model is not a good fit for our observed data.

Homework Answers

Answer #1

1. State the hypotheses for testing if the professor's predictions were inaccurate.

?0: The professor's predictions were accurate. The difference is due to chance.

??: The professor's predictions were not accurate. The difference is not due to chance.

2.

Expected Value = n * pi

Book Format Expected Value
Hard Copy 152 * 0.15 = 22.8
Print from Web 152 * 0.40 = 60.8
Read Online 152 * 0.45 = 68.4

3.

chi-squared statistic =

= 1.4795

4.

Degrees of freedom = k-1 = 3 - 1 = 2

5.

P-value = P( > 1.4795, df = 2) = 0.4772

6.

Since p-value is greater than 0.05 significance level, there is a
A. little evidence
that the null model is not a good fit for our observed data.

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