Reference no: EM13867153

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

a) Based on this sample, what is the 95% confidence interval for the proportion of bags that are overweight?

b) 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?

2) 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 # candiescounted
Apple hardcandy	15	50
Chocolatechew	18	50
Nutcluster	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.

a) Construct a contingency table for these data.

b) 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 conclusion.)

3) 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 March.)

(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 inJan	Sales inMarch	Change insales
1	Eastcoast	195	230	35
2	Eastcoast	220	240	20
3	Eastcoast	220	250	30
4	Eastcoast	245	265	20
5	Westcoast	130	157	27
6	Westcoast	130	140	10
7	Westcoast	80	99	19
8	Westcoast	185	207	22
Summarystatistics                                                     Allstores
Mean		175.63	198.50	22.88
SD		56.72	59.65	7.68
Eastcoast
Mean		220.00	246.25	26.25
SD		20.41	14.93	7.50
Westcoast
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.

a) Looking at all the stores, is there a difference in sales between January and March?

b) Did the campaign have a different effect on sales for stores on the East coast versus on the West coast?

c) Was there a difference in sales in January for stores on the East coast versus on the West coast?

4) 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	AppraisedLand Value	Appraised Valueof Improvements	SalePrice	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

a) 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.

b) 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 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.)

c) 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.)

d) 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.

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

e) 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 conclusion.

f) 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.)