ECO602 Assignment No  Solution Autumn 2021-22-02-2022 - video Dailymotion

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ECO602 Assignment No  Solution Autumn 2021-VU-Forecasting and Budgeting

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00:00bismillahirrahmanirrahim assalamualaikum dear viewers and students welcome to my

00:04youtube channel digital network today's assignment is related to the subject

00:08forecasting and budgeting with subject code eco 602 and it is due to the

00:15semester for 2021 it's mark is 10 and this last day for

00:232022 so let's start reading question and it's related requirement then we will

00:33jump into the solution the textile industry in Pakistan is growing over the

00:38time due to the modernization high standards of living and increasing

00:42demand for goods good material at reasonable price many fabric banks sorry

00:49many fabric brands like as Gul Ahmed, Sana Safina, Warda, Nadia Hussain, Firdos

00:58Lawn, Baris Lawn, Khaddi, Kaseria, Nishat and many others joined jointly made the

01:06textile industry in Pakistan they have mentioned some of the name top some of

01:13the top names of the textile industry here J dot is a famous ready-to-wear

01:21clothing brand in Pakistan offering variety of collection for men women and

01:25kids this is one of the famous brand in Pakistan which provides handmade

01:30garments to all classes of the society means men women and kids this brand

01:39started its business in 2002 with an outlet at Tariq Road, Karachi it has

01:44gained popularity in a very short period of time because of quality product and

01:50exclusive design consider the data given below and answer the following questions

01:55so let's go to the requirement number one spores monthly sales which is noted

02:02by Y of this brand with brand J dot depend on advertising expenses which is

02:08labelled by X monthly sale is dependent variable and advertising expenses

02:14independent variable here we state that the sale is depend on advertisement

02:21expense which is independent variable means sale is a function of

02:25advertisement function if the brand does not pay any amount on advertisement

02:33expense means X is equal to 0 then its monthly sales are Rs 1.5 million the

02:40value of beta of advertising expense is 3.5 the value of beta as given 3.5 you

02:47are required to which is also called slope of the line slope beta you are

02:53required to develop least scare regression equation to forecast monthly

02:59sales of this brand in this step you are going to make up a regression

03:05equation least scare regression equation based on the given information to

03:11perform a forecasting for monthly sales for J dot brand also find how much

03:18sales revenue will increase due to an increase in advertising expenses by

03:23rupees 300,000 now the second scenario is that how much your sales is affected

03:34by the spending 300 mount on advertisement campaign so on these two

03:43parts have these two number these number three plus two so now we are going to

03:50focus on required number two as monthly sales why of this brand depend on

03:56advertisement expense so consider monthly sales as dependent variable and

04:01advertising expense as independent variable the data related to the monthly

04:05sales why and advertising expense X is given in the table this one is the table

04:12by using this data in least scare regression equation which we have

04:16developed in part one so calculate slope of the line beta if you look at

04:25the required number one the value of beta is given but here we are going to

04:30calculate and regression coefficient alpha which is also called intercept

04:36step wise calculation is required now see n is equal to 50 mission XY is equal

04:43to 800,000 submission X into submission Y is equal to 3 million submission X

04:53K is equal to 200,000 submission X whole scale is equal to 360,000 submission X

04:59600,000 submission Y 5000 if you multiply submission X and submission Y you will

05:03get this value this value submission X and submission Y which is 3 million

05:10again it has two kinds of marks 3 plus 2 3 is related to the first task of part 2

05:20requirement 2 and 2 is the second task of the requirement 2 so without wasting

05:26time let's move to the solution here is the solution so we on the basis of the

05:41information given a point number one I have developed the least regression

05:46equation for J, J dot 1 is here Y is equal to beta naught plus beta 1 X this

05:55label as equation A or any other name that you like so why I use beta naught

06:00beta 1 because they have used these numbers in this equation beta 1 or beta

06:07so I in as per this notation I have used beta naught and beta 1 otherwise you

06:15can use anything in general what you like and Y says as per given

06:21information X is the advertisement expense also given here X is the

06:26independent variable and Y is the dependent variable which is depend on X

06:33Y is equal to the equation is the least square least square regression equation

06:40is 1.5 plus beta 1 is equal to X here two things maybe remember that sorry not

06:55two things I am considering X Y is equal to here as a constant intercept which is

06:591.5 million and beta 1 is equal to X he he described in the situation that if X

07:06is equal to 0 it means J dot does not expense any penny on advertisement so it

07:14will be equal to 0 so 3.5 into 0 is 0 and we will get the monthly sales 1.5

07:22million but when J dot brand plans to invest 300,000 on advertisement expense

07:30then what will be happened Y is equal to 1.5 million plus 3.5 into we are going

07:36to put the value of X is equal to 300,000 here so by multiplying and then

07:41adding we will get 2.55 million means 25,50,000 now we are going to part 2

07:51least linear regression model which has been developed in on the basis of

07:57information is Y is equal to alpha plus beta X is equal to 1 because now he is

08:04using alpha and this and relate this information with this 1.5

08:10million because here we are going to calculate this one also the equation is

08:15same as we as we use in part 1 so here I use beta 1 there he told that beta only

08:25so you can replace beta naught with alpha plus beta 1 with only beta so

08:32there's no difference here you can also use beta naught we cannot use beta naught

08:36here because as per restriction of the requirement he asking for alpha so if he

08:42doesn't mention anything so you can use any notation as you like but as per

08:47restriction we are going to be use alpha so it is better to use alpha here and

08:53beta alpha plus beta please understand the concept huh and don't confuse in

09:01notations so again Y is equal to monthly sales X is equal to advertisement expense and

09:06sales monthly sales depend on advertisement expense so alpha is the intercept

09:11beta is equal to slope of the line which line this way a line here beta is

09:18equal to n summation XY minus summation X into summation Y divided by n

09:23summation X K minus summation X whole K as the values are very huge so I am

09:32going to apply the concept of scaling by multiplying and divided and this right

09:38hand side element by 10,000 to reduce the values so 50 into summation XY 80

09:45thousand sorry 80 by dividing 10,000 minus 300 divided by 50 into summation X

09:56whole K which is 20 minus 36 which is summation X whole K let me check again

10:13this one

10:21yes 36 once again I have check this one so by solving this element inside the

10:30square braces we will get this 30 3700 divided by 964 into 10,000 and beta value

10:39is 38,381.74 actually the value is not very high but in this case it is very

10:47high I don't know because we have uses a very high value in this formula so

10:55please check with your own with your friends all these given information are

11:00correct on the basis of this information I have calculated this one so for alpha

11:06we need to we need X bar Y bar which is X mean Y mean so X bar is equal to

11:12summation X divided by n Y bar is equal to summation Y over n 600 divided by 50

11:195000 divided by 50 because n is equal to 50 we get X bar is equal to 12 and Y bar

11:27is equal to 100 thus alpha is equal to 100 Y bar minus beta X bar beta value is

11:3638,381.74 into 12 100 minus 460,580.88

11:45then we will get minus minus 460 480 460,480.88

11:53thus the required model is at least equation equation becomes Y is equal to

11:59minus 460 4 sorry minus 460,480.88 plus 38,381.74

12:11so this is your predicted fitted lines here you put the values of X which is

12:21presenting the advertisement expenses you will get their monthly sales so if

12:28someone find some typo error or in calculation any error please mention it

12:36in the comment section if someone contributes his knowledge to improve

12:41this solution welcome and drop his suggestions in comment section thanks

12:46for watching Assalamu Alaikum

ECO602 Assignment No Solution Autumn 2021-22-02-2022

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