e. Use PSPP/SPSS to calculate the Chi Square value. Use the instructions on pages 304-305. Copy/paste the output table. Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Age * Strength 138 100.0% 0 0.0% 138 100.0% Age * Strength Crosstabulation Strength Strong Moderate Weak Total Age Young Count 22 18 12 52 Expected Count 17.7 18.8 15.4 52.0 Middle Count 20 22 20 62 Expected Count 21.1 22.5 18.4 62.0 Old Count 5 10 9 24 Expected Count 8.2 8.7 7.1 24.0 Total Count 47 50 41 138 Expected Count 47.0 50.0 41.0 138.0 Chi-Square Tests Asymptotic Significance Value df (2-sided) Pearson Chi-Square 3.9692 4 .410 Likelihood Ratio 4.110 4 .391 Linear-by-Linear 3.628 1 .057 Association N of Valid Cases 138 a. O cells (0.0%) have expected count less than 5. The minimum
e. Use PSPP/SPSS to calculate the Chi Square value. Use the instructions on pages 304-305. Copy/paste the output table. Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Age * Strength 138 100.0% 0 0.0% 138 100.0% Age * Strength Crosstabulation Strength Strong Moderate Weak Total Age Young Count 22 18 12 52 Expected Count 17.7 18.8 15.4 52.0 Middle Count 20 22 20 62 Expected Count 21.1 22.5 18.4 62.0 Old Count 5 10 9 24 Expected Count 8.2 8.7 7.1 24.0 Total Count 47 50 41 138 Expected Count 47.0 50.0 41.0 138.0 Chi-Square Tests Asymptotic Significance Value df (2-sided) Pearson Chi-Square 3.9692 4 .410 Likelihood Ratio 4.110 4 .391 Linear-by-Linear 3.628 1 .057 Association N of Valid Cases 138 a. O cells (0.0%) have expected count less than 5. The minimum
e. Use PSPP/SPSS to calculate the Chi Square value. Use the instructions on pages 304-305. Copy/paste the output table. Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Age * Strength 138 100.0% 0 0.0% 138 100.0% Age * Strength Crosstabulation Strength Strong Moderate Weak Total Age Young Count 22 18 12 52 Expected Count 17.7 18.8 15.4 52.0 Middle Count 20 22 20 62 Expected Count 21.1 22.5 18.4 62.0 Old Count 5 10 9 24 Expected Count 8.2 8.7 7.1 24.0 Total Count 47 50 41 138 Expected Count 47.0 50.0 41.0 138.0 Chi-Square Tests Asymptotic Significance Value df (2-sided) Pearson Chi-Square 3.9692 4 .410 Likelihood Ratio 4.110 4 .391 Linear-by-Linear 3.628 1 .057 Association N of Valid Cases 138 a. O cells (0.0%) have expected count less than 5. The minimum
In Chapter 17 Data Set 5, you will find entries for two variables: age category (young, middle-aged, and old) and strength following weight training (weak, moderate, and strong). Are these two factors independent of one another?
Strength
Weak
Moderate
Strong
Age
Young
12
18
22
Middle
20
22
20
Old
9
10
5
What is the null hypothesis in words? (You will need to get this from lecture and/or the lecture notes. It is not covered in your text).
What is the alternative hypothesis in words? (See note in a. above)
What is your degrees of freedom for the test? How did you arrive at this answer?
Based on c. above and using an alpha =.05, what is the critical value for the test?
Use PSPP/SPSS to calculate the Chi Square value. Use the instructions on pages 304-305. Copy/paste the output table. (result in the pictures)
What is the Chi Square value? What is the p-value?
Based on f., do you reject or fail to reject the null hypothesis? Why? Justify your answer.
In everyday language, what does this mean about the results of the test?
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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