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For the given data set representing the runs scored by two players in last 15 matches On the basis of the following data, the marketing manager wants to predict the |
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NMIMS Global Access
School for Continuing Education (NGA-SCE)
Course: Business Statistics
Internal Assignment Applicable for June 2020 Examination
Assignment Marks: 30
Instructions:
- All Questions carry equal marks.
- All Questions are compulsory
- All answers to be explained in not more than 1000 words for question 1 and 2 and for question 3 in not more than 500 words for each subsection. Use relevant examples, illustrations as far as possible.
- All answers to be written individually. Discussion and group work is not advisable.
- Students are free to refer to any books/reference material/website/internet for attempting their assignments, but are not allowed to copy the matter as it is from the source of reference.
- Students should write the assignment in their own words. Copying of assignments from other students is not allowed
- Students should follow the following parameter for answering the assignment questions
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|
Match No. |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
Player A |
8 |
42 |
56 |
68 |
91 |
123 |
12 |
46 |
57 |
137 |
5 |
80 |
14 |
10 |
19 |
|
Player B |
38 |
44 |
46 |
59 |
57 |
61 |
48 |
42 |
51 |
39 |
58 |
41 |
55 |
45 |
68 |
1. For the given data set representing the runs scored by two players in last 15 matches, conduct the following analysis:
i. Which average you will use to summarize the performance of the player? Find average runs scored for both of the players. Also give reasons for the choice of the average?
ii. If selection is possible on the basis of consistency, which player would you choose in the team? Perform the required statistics and justify the selection.
iii. Check whether there exists any relationship among the runs scored by two players using Karl Pearson coefficient of correlation and interpret the same. (10 Marks)
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2. On the basis of the following data, the marketing manager wants to predict the sales volume for the locality on the basis of # households, number of cars and marketing expense Sl.No. Sales Volume #Households number of cars marketing expense
|
Sl.No |
Sales Volume |
#Households |
number of cars |
marketing expense |
|
1 |
15727 |
161 |
3 |
180 |
|
2 |
9328 |
99 |
1 |
150 |
|
3 |
13681 |
135 |
2 |
175 |
|
4 |
12379 |
120 |
2 |
165 |
|
5 |
15351 |
164 |
3 |
178 |
|
6 |
24174 |
221 |
5 |
220 |
|
7 |
20154 |
179 |
4 |
205 |
|
8 |
20671 |
204 |
5 |
210 |
|
9 |
22978 |
214 |
5 |
128 |
|
10 |
13522 |
101 |
1 |
176 |
|
11 |
22471 |
231 |
5 |
226 |
|
12 |
19529 |
206 |
4 |
296 |
|
13 |
24216 |
248 |
3 |
240 |
|
14 |
11521 |
107 |
2 |
168 |
|
15 |
197.82 |
205 |
3 |
100 |
i. Draw three scatter plots of sales volume with each of the three variables and comment on their correlation.
ii. Regress the sales volume on #household, number of cars and marketing expense. Calculate R square and interpret the same.
iii. Determine which variable is/are significant variable/s. Is there any insignificant variable? If yes, regress again, by dropping the variable. Will dropping that variable increases the adjusted R square? (10 Marks)
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3. a. The height of the students in a certain class is following normal distribution with mean height as 165 cm and standard deviation of 25 cm. There are 60 students in that class. Determine
i. The number of students whose height is more than 158 cm.
ii. The number of students whose height is lying between 155 and 172 cm. (5 Marks)
3. b. Find the lowest height among the tallest 5 student in a class. (5 Marks)
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