NMIMS Global Access
School for Continuing Education (NGA-SCE)
Course: Decision Science
Internal Assignment applicable for June 2024 Examination
Assignment Marks: 30
1. In the world of social media, understanding the factors that contribute to the number of likes on a post is crucial for content creators. Let’s consider a scenario where you want to predict the number of likes on Instagram posts based on two variables: the number of followers and the length of the caption.
Post
Likes
Followers
Caption Length
1
120
5000
150
2
85
5500
180
3
100
6000
200
4
110
4500
160
5
95
7000
140
6
130
5200
170
7
75
5800
190
8
115
6300
150
9
80
4800
180
10
150
7500
160
11
105
5100
130
12
90
6700
170
13
125
5900
200
14
70
6800
150
15
140
5400
180
16
95
7200
160
17
120
4600
140
18
110
7100
170
19
100
5600
180
20
145
8000
120
Note: Well, you must do these calculations using EXCEL and write the interpretation of the following.
• Hypothesized regression model
• R-square adjusted
• Multiple R
• ANOVA Table
• Significance of Regression coefficients. (10 Marks)
2. Samantha Patel, employed as an analyst in a renowned technology firm, is contemplating investing her savings in the stock market. Recommendations from her friends, who possess expertise in stock market investments, led her to consider investing in ‘TechGen’ and ‘InnovateCorp’ shares. An economist friend, Rahul Kapoor, has outlined four different scenarios regarding potential returns on Samantha’s investments. The payoff figures for one unit of share in INR for each scenario are as follows:
Payoff (Profit within one month on one unit of share in INR)
Scenario 1 (s1)
Scenario 2 (s2)
Scenario 3 (s3)
Scenario 4 (s4)
TechGen
48
35
22
15
Innovate Corp
32
40
45
53
i. Set up the opportunity loss table based on the provided payoff figures.
ii. Create a decision tree illustrating the decision-making process for Samantha’s investment. You may use any software for making the tree diagram, but a handwritten snapshot will be unacceptable.
In making of this tree show the payoff values given above only.
iii. According to Rahul Kapoor’s latest research, he has assigned the following probabilities to the four scenarios (states of nature):
P(S1) = 0.3
P(S2) = 0.4
P(S3) = 0.2
P(S4) = 0.1
Determine the Expected Monetary Value (EMV) decision based on the probabilities assigned by Rahul. (10 Marks)
3. Using the following data and analyze in EXCEL. YearYear Rice (Lakh Rice (Lakh hectares)hectares) Wheat Wheat (Lakh (Lakh hectares)hectares) Coarse Coarse Cereals Cereals (Lakh (Lakh hectares)hectares) Pulses Pulses (Lakh (Lakh hechectares)tares) 19661966–6767 353353 128128 451451 221221 19671967–6868 364364 150150 473473 227227 19681968–6969 370370 160160 462462 213213 19691969–7070 377377 166166 472472 220220
YearYear Rice (Lakh Rice (Lakh hectares)hectares) Wheat Wheat (Lakh (Lakh hectares)hectares) Coarse Coarse Cereals Cereals (Lakh (Lakh hectares)hectares) Pulses Pulses (Lakh (Lakh hechectares)tares) 19701970–7171 376376 182182 460460 225225 19711971–7272 378378 191191 436436 222222 19721972–7373 367367 195195 422422 209209 19731973–7474 383383 186186 462462 234234 19741974–7575 379379 180180 432432 220220 19751975–7676 395395 205205 438438 245245 19761976–7777 385385 209209 419419 230230 11977977–7878 403403 215215 423423 235235 19781978–7979 405405 226226 422422 237237 19791979–8080 394394 222222 414414 223223 19801980–8181 402402 223223 418418 225225 19811981–8282 407407 221221 425425 238238 19821982–8383 383383 236236 404404 228228 19831983–8484 412412 247247 417417 235235 19841984–8585 412412 236236 392392 227227
YearYear Rice (Lakh Rice (Lakh hectares)hectares) Wheat Wheat (Lakh (Lakh hectares)hectares) Coarse Coarse Cereals Cereals (Lakh (Lakh hectares)hectares) Pulses Pulses (Lakh (Lakh hechectares)tares) 19851985–8686 411411 230230 395395 244244 19861986–8787 412412 231231 397397 232232 19871987–8888 388388 231231 366366 213213 19881988–8989 417417 241241 387387 232232 19891989–9090 422422 235235 377377 234234 19901990–9191 427427 242242 363363 247247 19911991–9292 427427 233233 334334 225225 19921992–9393 418418 246246 344344 224224 19931993–9494 425425 252252 328328 223223 19941994–9595 428428 257257 322322 230230 19951995–9696 428428 250250 309309 223223 19961996–9797 434434 259259 318318 225225 19971997–9898 435435 226767 308308 229229 19981998–9999 448448 275275 293293 235235 19991999–0000 452452 275275 293293 211211
YearYear Rice (Lakh Rice (Lakh hectares)hectares) Wheat Wheat (Lakh (Lakh hectares)hectares) Coarse Coarse Cereals Cereals (Lakh (Lakh hectares)hectares) Pulses Pulses (Lakh (Lakh hechectares)tares) 20002000–0101 447447 257257 303303 204204 20012001–0202 449449 263263 295295 220220 20022002–0303 412412 252252 270270 205205 20032003–0404 426426 266266 308308 235235 20042004–0505 419419 264264 290290 228228 20052005–0606 437437 265265 291291 224224 20062006–0707 438438 280280 287287 232232 20072007–0808 439439 280280 285285 236236 20082008–0909 455455 278278 275275 221221 20092009–1010 419419 285285 277277 233233 20102010–1111 429429 291291 283283 264264 20112011–1212 440440 299299 264264 245245 20122012–1313 428428 300300 248248 233233 20132013–1414 440440 312312 257257 252252 20142014–1515 439439 310310 242242 231231
YearYear Rice (Lakh Rice (Lakh hectares)hectares) Wheat Wheat (Lakh (Lakh hectares)hectares) Coarse Coarse Cereals Cereals (Lakh (Lakh hectares)hectares) Pulses Pulses (Lakh (Lakh hechectares)tares) 20152015–1616 435435 304304 244244 249249 20162016–1717 440440 308308 250250 294294 20172017–1818 438438 297297 243243 298298 20182018–1919 442442 293293 221221 292292 20192019–2020 437437 314314 240240 280280 20202020–2121 458458 311311 241241 288288 20212021–2222 463463 305305 227227 307307 20222022–2323 477477 318318 236236 291291
Data source: RBI
a. Which pattern is visible in all the crops across these many years? Suggest appropriate chart for this pattern detection task. (5 Marks)
b. Identify two pairs of combination of the crops having negative correlations? Which graph will help you to detect that? provide that graph also. (5 Marks)