Need Help-SPSS Project


 Need Help-SPSS Project

Follow all instructions carefully in presenting your answers. Be sure to show all your working. (Handwritten responses are fine.) You will not need SPSS for questions 1-3. For question 4, please download the housing dataset (from Latte), then import it into SPSS for analysis.

 

 

 

  • Jet Blue Airlines examined the bags of 80 passengers and found that 20% of the bags were overweight.

 

  1. Based on this sample, what is the 95% confidence interval for the proportion of bags that are overweight? [6 points]

 

  1. What is the minimum sample size the airline would need to estimate with 95% confidence to obtain a margin of error of +/- 3% for this estimate of the percentage of overweight bags? [6 points]

 

 

 

  • A factory recently took a sample to assess the quality of its candy output, looking at three different types of candy, and how many of each type of candy were damaged during the manufacturing process:

 

Candy # damaged Total # candies counted
Apple hard candy 15 50
Chocolate chew 18 50
Nut cluster 30 100

 

The factory management would like to determine whether the proportion of candy that is damaged is different for these three types of candy.

  1. Construct a contingency table for these data. [2 points]

 

  1. Is the proportion of candy that is damaged different for these three types of candy? (Calculate the appropriate statistic, give the p-value, and state your ) [6 points]

 

  • A manufacturer of headphones is interested in the sales of a particular headphone model in its stores in 8 airports. Some of these stores are located on the West and some on the East coast of the U.S. Also, the manufacturer recently conducted an advertising campaign. The sales before and after the advertising campaign, which it ran in February using billboards in the airports, are shown below (i.e., data for sales in those stores in January and data for sales in the same stores for )

 

(Some descriptive statistics have also been provided in the table. You will need to decide which ones you need for your calculations in answering the questions below.)

 

Store Location Sales in Jan Sales in March Change in sales
1 East coast 195 230 35
2 East coast 220 240 20
3 East coast 220 250 30
4 East coast 245 265 20
5 West coast 130 157 27
6 West coast 130 140 10
7 West coast 80 99 19
8 West coast 185 207 22
                                                  Summary statistics                                                   

All stores

Mean 175.63 198.50 22.88
SD 56.72 59.65 7.68
East coast      
Mean 220.00 246.25 26.25
SD 20.41 14.93 7.50
West coast      
Mean 131.25 150.75 19.50
SD 42.89 44.71 7.14

 

To get full points when answering each part below be sure to: calculate an appropriate statistic, state the result of the test, and state your conclusion.

  1. Looking at all the stores, is there a difference in sales between January and March? [6 points]
  2. Did the campaign have a different effect on sales for stores on the East coast versus on the West coast? [6 points]
  3. Was there a difference in sales in January for stores on the East coast versus on the West coast? [6 points]

 

  • Below are data for 40 houses located in one of two neighborhoods (A or B).

(This data is also provided in an Excel spreadsheet on the website for the class. Open the data in SPSS and conduct the analyses required to answer the questions. Be sure to paste output (i.e., tables) from SPSS into your answers where that is requested or else you will lose points.)

 

 

Neighborhood

Appraised Land Value Appraised Value of Improvements  

Sale Price

Has a yard? (yes/no)
A 56658 53806 255000 no
A 93200 11121 422000 no
A 76125 78172 290000 no
A 28996 5864 305900 no
A 30000 64831 118500 yes
A 30000 50765 93900 yes
A 46651 8573 191500 yes
A 45990 91402 184000 yes
A 42394 98181 168000 yes
A 47751 3351 169000 yes
A 63596 2182 208500 yes
A 51428 72451 264000 yes
A 54360 61934 237000 yes
A 65376 34458 286500 yes
A 42400 15046 202500 yes
A 40800 92606 168000 yes
A 12170 22786 375000 yes
A 24637 90598 169900 yes
A 30600 80858 135000 yes
A 44730 99047 176000 yes
B 38979 25946 140000 no
B 14861 59258 74900 no
B 14976 48957 57300 no
B 15244 55169 87500 no
B 18260 59267 82000 no
B 16680 55525 78000 no
B 53421 19792 175000 no
B 31417 99413 185000 no
B 32311 75343 123000 no
B 26817 78726 108000 no
B 24564 66533 108000 no
B 24564 71149 112900 no
B 27640 85347 106000 no
B 29656 78968 147500 no
B 13440 41177 61000 yes
B 45765 81227 320000 yes
B 16680 72867 99500 yes
B 17020 61935 93000 yes
B 25751 82259 110000 yes
B 25751 64568 100500 yes

 

 

  1. Give appropriate summary statistics (one measure of central tendency and one measure of

 

variation) for each of the 3 variables Appraised Land Value, Appraised Value of Improvements, and Sale Price, calculated separately for neighborhoods A and B. Important: PROVIDE ONLY ONE (APPROPRIATE) CENTRAL TENDENCY MEASURE AND ONE (APPROPRIATE) MEASURE OF VARIATION FOR EACH VARIABLE FOR EACH NEIGHBORHOOD. [6

points]

 

  1. Based on this data sample, do neighborhoods A and B differ in the number of houses with and without yards? In your answer be sure to calculate an appropriate statistic, state the result of the test, and state your (Paste the output from SPSS for the statistical test that you do in your answer, as well as stating your conclusion and writing out the appropriate statistic that supports your conclusion.) [6 points]

 

  1. Based on this data sample, do houses in neighborhoods A and B have different sale prices? (In your answer be sure to calculate an appropriate statistic, state the result of the test and state your conclusion.) (Paste the output from SPSS for the statistical test that you do in your answer, as well as stating your conclusion and writing out the appropriate statistic that supports your conclusion.) [6 points]

 

  1. Provide a correlation matrix for Appraised Land Value, Appraised Value of Improvements and Sale Price for neighborhood B only (you will need to split the data to do this – in SPSS under the Data menu use the “split file” command, split by neighborhood, and select “organize output by groups”). In words, explain the meaning of the correlation between Sale price and Appraised Land Value and the meaning of the correlation between Appraised Land Value and Appraised Value of Improvements. [6 points]

 

Note: make sure you deselect “split file” after doing this question part, so that you analyzing all the cases for the next two parts.

 

  1. Imagine you are interested in the relationship between house Sale price and Appraised Land Value while controlling for any effects of Appraised Value of Improvements. Conduct a linear regression that allows you to test this relationship (using data for all the houses, i.e., from both neighborhoods). State your conclusion about the relationship, and provide the statistics that support your (Paste your SPSS output for this regression into your answer.) [6 points]

 

  1. Imagine you are interested in the relationship between house Sale price and Neighborhood, while controlling for any effects of Appraised Land Value and Appraised Value of Improvements on Sale price. Conduct a linear regression that allows you to test this relationship. State your conclusion about the relationship, and provide the statistics that support your conclusion. (Paste your SPSS output for this regression into your answer.) [6 points]

 

 

Excel Data

Neighborhood Appraised Land Value Appraised Value of Improvements Sale Price Has a yard? (yes/no)
A 56658 53806 255000 no
A 93200 11121 422000 no
A 76125 78172 290000 no
A 28996 5864 305900 no
A 30000 64831 118500 yes
A 30000 50765 93900 yes
A 46651 8573 191500 yes
A 45990 91402 184000 yes
A 42394 98181 168000 yes
A 47751 3351 169000 yes
A 63596 2182 208500 yes
A 51428 72451 264000 yes
A 54360 61934 237000 yes
A 65376 34458 286500 yes
A 42400 15046 202500 yes
A 40800 92606 168000 yes
A 12170 22786 375000 yes
A 24637 90598 169900 yes
A 30600 80858 135000 yes
A 44730 99047 176000 yes
B 38979 25946 140000 no
B 14861 59258 74900 no
B 14976 48957 57300 no
B 15244 55169 87500 no
B 18260 59267 82000 no
B 16680 55525 78000 no
B 53421 19792 175000 no
B 31417 99413 185000 no
B 32311 75343 123000 no
B 26817 78726 108000 no
B 24564 66533 108000 no
B 24564 71149 112900 no
B 27640 85347 106000 no
B 29656 78968 147500 no
B 13440 41177 61000 yes
B 45765 81227 320000 yes
B 16680 72867 99500 yes
B 17020 61935 93000 yes
B 25751 82259 110000 yes
B 25751 64568 100500 yes

Need Help-SPSS Project

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