Question

ANALYSIS An Australian manufacturing company is keen to develop new products and develop new product line of shoes so that the company can expand into Asian markets more than before. The data have been collected for 99 products from the market. The data are in an excel file named “SHOES”. The file includes: PRICE: Price of the shoes in dollars. GENDER: 1 for Female product and 2 for Male product. COUNTRY: the country in which the shoes is produced: 1 made in Thailand, 2 made in Singapore and 3 made in China. COST: Production cost of the shoes in dollars.

questions:

EXCEL Instructions Activation of Data Analysis:

1. Click on File, then click Options.

2. Click Add-Ins, and then in the Manage box, select Excel Add-ins.

3. Click Go.

4. In the Add-Ins available box, select the Analysis ToolPak check box, and then click OK.

5. After you load the Analysis ToolPak, the Data Analysis command is available in the Analysis group on the Data tab.

Filter numbers: Highlight the column of data/ Data tab/Sort/expand the selection / A/Z Graphs:

1. Highlight all data including labels

2. Insert Tab/ choose the chart. Summary statistics From Menu, Data Tab, Data analysis, Descriptive statistics (highlight data for input range), tick summary statistics and confidence interval, OK Two sample hypothesis testing:

Excel/Data/Data Analysis/ t test, two sample assuming equal variances (or unequal)/ highlight data for variable 1 and 2/ insert the value for Hypothesized difference equal zero and level of significance 0.05.

*if you have highlighted the labels tick (the relevant box. Anova: From Menu, Data Tab, Data analysis, Anova single factor highlight data for input range), insert the value for alpha, OK Regression From Menu, Data Tab, Data analysis, Regression, highlight data for Input Y range and Input X range, tick Residuals Plots boxes, OK

COST	GENDER	COUNTRY	PRICE
177	1	1	395
143	1	1	400
163	1	1	304
186	1	1	274
124	1	1	371
43	1	1	355
112	1	1	154
186	1	1	261
124	1	1	258
43	1	1	280
112	1	1	240
186	1	1	314
124	1	1	273
112	1	1	366
265	1	1	372
185	1	1	353
223	2	1	314
213	2	1	294
183	1	1	222
173	1	2	182
143	1	2	161
143	1	2	193
163	1	2	260
133	1	2	198
207	1	2	215
265	1	1	353
133	2	1	386
123	2	1	303
29	2	1	225
29	2	1	171
193	2	1	291
183	2	1	350
139	2	1	315
133	1	2	308
73	1	2	156
83	1	2	313
159	1	2	364
188	1	2	192
139	1	2	151
89	1	2	390
75	1	2	211
69	1	2	306
75	1	2	210
69	1	2	334
55	1	2	247
55	1	3	341
55	1	3	238
41	1	3	299
49	1	3	183
51	1	2	200
45	1	2	271
65	1	2	350
65	1	2	361
43	1	2	250
43	2	2	244
185	2	2	274
185	2	2	388
183	2	2	348
117	2	2	163
111	1	2	172
177	1	2	399
97	2	2	360
69	2	2	244
57	2	2	233
65	2	2	319
36	2	2	337
38	1	3	259
34	1	3	361
36	1	3	381
183	2	3	323
38	2	3	231
138	2	3	205
199	2	1	289
243	2	1	297
163	2	1	356
252	2	1	345
223	2	1	298
213	2	1	285
153	2	1	311
159	2	1	269
188	2	1	340
36	2	3	276
46	2	3	373
126	2	3	287
66	2	3	367
48	2	3	157
116	1	3	155
193	1	3	335
183	1	3	367
139	1	3	345
117	2	3	153
111	2	3	396
69	2	3	277
66	2	3	246
175	2	3	352
155	2	3	380
74	2	3	278
66	2	3	153
175	2	3	209

Answer 1

Using data analysis toolpak for analysing the data as mentioned in the question:

from the descriptive statistics table the summary statistics including confidence intervel for all the four variables are given.

The table above gives the results of the t test for two samples cost and price assuming equal variance at 5% level of significance are given.

The value of test statistic is -16.71308 and the critical vaue of the test statistic is 1.97214 .

since the absolute value of the test statistic is greater than critical value so we conclude that there is significant effect between cost and price.

The above table gives the results of anova single factor between the four variables cost ,gnder country and price.

from the above table the value of the test statistic is 767.5111 and the critical value of the test statistic is 2.627672

since p value is less than 0.05 so we conclude that atleast one of the treatment means is different from the other.

The above table gives the Regression of the two variable cost and price along with the price residual plot.

from the summary output the regression equation can be written as:

cost = 70.28557 + 0.18514*price