Reference no: EM133797129

An analyst wonders if the age distribution of customers coming for service In Town is the same as at the Mall.
He selects transactions at random from each branch and researches the ages of the customers. These are the data :

Age
< 30 30 - 55 56 + Totals
Distribution In Town 24 37 35 96
Mall 29 49 30 108
Totals 53 86 65 204

For all problems, try to enter your answers or calculations in the yellow cells.

Question 1 Write the null hypothesis regarding the frequency distribution of ages by In Town and Mall.

Which type of test is required (goodness of fit, independence, homogeneity)?

Question 2 Compute
Age Groups
< 30 30 - 55 56 +
Expected (E) In Town
Mall

Age Groups
< 30 30 - 55 56 +
(O - E)2/E In Town
Mall

Question 3 What is the chi square test statistic? Note: =CHISQ.TEST does not compute the chi-squared test statistic. It computes p-value.

Question 4 What is the degrees of freedom of the chi-squared test statistic?

Use an Excel function to compute the critical value at alpha = 10%.

What is the percent p-value of the chi square test statistic?

Question 5 State your decision about H0 with the significance level.

Are the bank distributions the same (Yes/No)?

Moto owns a silk shop in Taipei City and wants to expands color selection.
Before expanding colors, Moto will decide if the customers have a brand preference.
As an experiment, the number of sales by brand were recorded on randomly selected days.
Can it be concluded that there is a difference in preference among the brands at the significance level 10%

Cyan Pearl
724 776
709 640
798 822
845 812
720 673
800 893

Question 6 What is the null hypothesis?

What is the alternative hypothesis?

Question 7 The level of significance depends on the application. What is the level of significance (α) in this problem?

Perform single factor ANOVA using Data Analysis. Include the labels in row 6 in the Input Range by checking the Labels checkbox.

Question 8 From the ANOVA output: What is the F test statistic?

What is the critical value of the F test statistic?

Question 9 What is your decision in terms of H0, the significance level, p-value, the F test statistic, and the critical value of the F test statistic.

Studies have shown that the frequency with which shoppers browse Internet retailers is positively corelated to the frequency with which they make purchases.
The following data show respondents Age and Time in minutes browsing online retailers per year.
Age (X) Time (Y)
16 487
17 327
19 387
22 404
22 313
22 517
22 477
28 295
28 447
28 475
28 636
30 397
33 621
34 624
35 686
35 584
35 635
36 765
39 708
39 526
40 723
42 622
43 527
44 593
48 619
50 574
50 675
51 771
52 442
54 635
58 708
59 789
60 771

Question 10 Compute the correlation between Age and Time using Data Analysis. Include the labels in the Input Range and check the Labels checkbox. Book Our Assignment Help Service Now!

Question 11 Compute the correlation using the Excel function =CORREL. If answers for #10 and 11 do not agree, there is an error.

Question 12 The strength of the correlation motivates further examination.
a) Make a scatter plot linked to and near the data above, and with Age on the horizontal (X) axis.
b) Add to your chart
A meaningful title
Vertical axis label Time
Horizontal axis label Age
c) Complete the chart by adding Trendline and checking boxes

Question 13 Read directly from the chart:
a) Intercept =
b) Slope =
c) R2 =

Question 14a Perform regression using Data Analysis. Select the Time data first, include the labels in row 4 in the Input Range , and check the Labels checkbox.

Question 14b In the Regression output, highlight the Y-intercept blue, the slope green, and R2 red.

Question 15 Is it valid to use this data to predict the amount of time that a 70-year-old will spend browsing online retailers ?

Why or why not (Week 11 Presentation, slide 8)?

Question 16 Is it valid to use this data to predict the amount of time that a 60-year-old will spend browsing online retailers ?

If valid, use the Data Analysis output to predict the number of minutes spent by a 35-year old shopper. Enter = followed by the regression formula,
entering the intercept and slope into the formula by clicking on the corresponding cells in the regression output.
(Week 11 Presentation, slide 11)

Question 17 Make a scatter plot linked to and near the following data:
X Y
0.5 7
1.01 2.5982
1.07 2.2752
1.16 2.592
1.19 2.2252
1.19 3.2843
1.22 2.19
1.32 3.6176
1.38 2.9516
1.43 3.9971
1.45 4.0855
1.46 4.0862
1.48 4.0272
1.56 4.3208
1.58 3.6052
1.6 3.59
1.72 4.8756
1.77 4.1042
1.8 4.538
1.81 5.1257
1.88 4.6192
1.94 5.4924
2 5.21

Question 18 Make the title Scatterplot of X and Y Data
Add trendline, regression equation, and R2 to the plot.

Question 19 "The scatterplot reveals a point outside the point pattern. A ""residual"" is defined as the veritical distance from the line to a point. Q1, Q3, and IQR were computed for the residuals. Residuals that are more that 1.5 IQR below Q1 or more than 1.5 IQR above Q3 are called outliers and the corresponding data must be investigated. It was determined that the outlying data point was due to data entry error. The data with outlier removed is copied below.

Make a new scatterplot linked to and near the cleaned data, and add title Scatterplot without Outlier, trendline, and regression equation."

X Y
1.01 2.5982
1.07 2.2752
1.16 2.592
1.19 2.2252
1.19 3.2843
1.22 2.19
1.32 3.6176
1.38 2.9516
1.43 3.9971
1.45 4.0855
1.46 4.0862
1.48 4.0272
1.56 4.3208
1.58 3.6052
1.6 3.59
1.72 4.8756
1.77 4.1042
1.8 4.538
1.81 5.1257
1.88 4.6192
1.94 5.4924
2 5.21

Question 20 Compare the regression results of the two plots. What does the outlier do to the regression line?
What does the regression line do when the outlier is removed?

Why does R2 increase when an outlier is removed?

Citation Style:APA