Open StatCrunch --> Input Observed Counts in Var1 and Expected Counts in Var2 --> Stat --> Goodness-of-fit --> Chi-Square test --> Select Var1 for Observed and select Var2 for Expected --> Compute! Since P-value (0.613) is not less than alpha, do not reject the null hypothesis. There is not sufficient evidence to warrant rejection of the distribution claimed by the manufacturer of M&Ms. Ch 12.2Tests for Independence for Independence Objective A :Tests Example 1: (a) Compute the expected values of each cell under the assumption of independence. By assuming an individual opinion and gender are independent, . row1 total = 196+199 = 395 row2 total = 239+249 = 488 col1 total = 196+239 = 435 col2 total = 199+249 = 448 Grand total = 196 + 199 + 239 + 249 = 883 Summarize the observed counts and expected counts in a table where the expected counts are expressed in a parenthesis.
Open StatCrunch --> Input Observed Counts in Var1 and Expected Counts in Var2 --> Stat --> Goodness-of-fit --> Chi-Square test --> Select Var1 for Observed and select Var2 for Expected --> Compute! Since P-value (0.613) is not less than alpha, do not reject the null hypothesis. There is not sufficient evidence to warrant rejection of the distribution claimed by the manufacturer of M&Ms. Ch 12.2Tests for Independence Objective A :Tests for Independence Example 1: (a) Compute the expected values of each cell under the assumption of independence. By assuming an individual opinion and gender are independent, . row1 total = 196+199 = 395 row2 total = 239+249 = 488 col1 total = 196+239 = 435 col2 total = 199+249 = 448 Grand total = 196 + 199 + 239 + 249 = 883 Summarize the observed counts and expected counts in a table where the expected counts are expressed in a parenthesis.