Suppose you are given a bag labeled as containing 3 red balls, 3 green balls, 1 white ball, 1 orange ball, and 2 blue balls. You randomly select a ball from the bag and record its color before replacing it in the bag. You repeat this process 100 times; your results are below. Do your data provide sufficient evidence to conclude that the label on the bag is incorrect about the contents of the bag? Perform the appropriate test at the 1% significance level. Please use SPSS to solve. COLOR OF BALLFREQUENCYRed42Green34White4Orange4Blue16

Given information: Color of Ball Frequency Frequency Red 42 Green 34 White 4 Orange 4 Blue 16Null hypothesis: H0: The label on the bag is correct about the contents of the bag. Alternate hypothesis: HA: The label on the bag is incorrect about the contents of the bag. For this analysis, the significance level is 0.01. We will conduct a chi-square goodness of fit test of the null hypothesis. Use the given data to form excel table consisting of observed frequency, expected probability, expected frequency and chi square statistics. Color of Ball Observed P(x) Expected (Or,c-Er,c)2Er,c Red 42 0.3 30 4.8 Green 34 0.3 30 0.533333333 White 4 0.1 10 3.6 Orange 4 0.1 10 3.6 Blue 16 0.2 20 0.8 Sum 100 1 100 13.33333333 Formula for the expected frequency is: Ex=n·P(x) DF = k - 1 D.F = 5 - 1 D.F = 4Use the table to determine the chi-square statistic, degree of freedom and P-value. χ2=∑(Or,c-Er,c)2Er,c =13.333333≈13.33Use the excel formula, "=CHISQ.DIST.RT(13.33,4)" for determining the P-value from the chi-square statistic having degrees of freedom 4. P(χ2>13.33)=0.0098 Since the P-value (0.0098) is less than the significance level (0.01), we have to reject the null hypothesis. We have to Reject H0. There is sufficient evidence to conclude that the label on the bag is incorrect about the contents of the bag.SPSS steps for chi square goodness of fit test: Step 1) Click Analyse > Non parametric Tests > Legacy Dialogs > Chi-square. Step 2) Transfer the Colour of ball variable into the Test Variable List Step 3) Insert the expected proportion of different categories as 0.3, 0.3, 0.1, 0.1 and 0.2 in Expected Values area. Step 4) Click OK button to generate the output. Output: