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Linear Regression (in Hindi)
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This lesson throws light on linear regression.

Harshit Aggarwal
Cleared UPSC ESE twice with Rank 63 and 90 in mechanical engg. Got 99 percentile in GATE. Cracked ONGC, BHEL,ISRO, SAIL, GAIL successfully

U
Unacademy user
Many congratulations to dear Chatterjee sir for your grand success on very first attempt. Your video is very helpful and inspiring and it is full of positive energy for those who thinks that carcking the exam in one year with a course curriculum of preimer college is next to impossible, but you did it and I hope many more will follow from next year onwards. In your video you said you worked as a free lancer for 5 years and that helped you to buold up your writting skills. I request you to make a special video over it so that many college students and others who lack in writting skills can follow your path and build their skills of writting. Thanks a lot for telling your inspirational journey.
sir last ques. me c right answer h
  1. COURSE: INDUSTRIAL ENGINEERING LESSON LINEAR REGRESSION


  2. ABOUT ME Graduated from NIT Nagpur in 2008 Cleared Engineering Services Examination (ESE-UPSC) Exam Got the offer letter from most of the Maharatna and Navratna Companies Cleared GATE Exam Rate, Review, Recommend, Share Follow me on Unacademy at: https://unacademv.in/user/harshit aggarwal


  3. LINEAR REGRESSION This is the mathematical method of obtaining the line of best fit between the dependent variable usually demand and independent variable.The relationship between the dependent variable Y and independent variable X can be represented by a straight line Where b is slope of line and a is the Y intercept. Then the value of a and b are calculated as follows: y- a+bx of bot sides Now, nuliplying both sicles


  4. LINEAR REGRESSION taking of both sides Special Cose Least spuave Metol When Pndependent van alle x trneer ond ts the yea os sales etc anol O c


  5. QUESTION A company manufacturing TV sets established relationship that its sales is related to population of city.The market research reveals following information. Fit a linear regression equation and establish the demand for TV sets for a city with a population of (50 million). X - Population Y - No. of TV sets (in 1000) (Million) 28 40 65 80 96 130 7 15 27 26


  6. SOLUTION XY Y - No. of TV sets (in 1000) X - Population (Million) 28 40 65 80 96 130 | 439 140 280 975 1760 2592 3380 | 9127 25 49 225 484 729 676 27 26 2X2 2188 | 102


  7. SOLUTION a- 23- b31-3.66 (62) 6 Y 193.7466: b = 6(9127)-(I-2)(43) 2324-


  8. QUESTION The demand and forecast for February are 12000 and 10275, respectively. Using single exponential smoothening method (smoothening coefficient = 0.25), forecast for the month of March is: (a) 431 (b) 9587 (c) 10706 (d) 11000


  9. SOLUTION Fn-1-10275, According to single exponential smoothing method d,,-12000, F-? F ad-1 (1-a)F0.25 x12000+0.75x10275 10706.25 Ans :d


  10. THANK YOU