Andros intercom
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Andros intercom

on

  • 231 views

 

Statistics

Views

Total Views
231
Views on SlideShare
231
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Andros intercom Presentation Transcript

  • 1. CASE STUDY – ANDROS INTERCOM Presented by – Bhawish Chowdhuary Ravi Kumar Rai Rhythyma Bhargava Swati Singh
  • 2. Personality Involved • Dinos Andros => Founder, President. • Fay Philmus => V.P, sales • JON Richman => V.P , Finance. • David => Assistant of Fay
  • 3. FACTS • LAST YEAR SALES UNDERESTIMATED = 16% • LESS MATERIAL SUPPLIES => LESS Production. • Year before => 21 %. • “New sales person , a new forecaster”
  • 4. Case : A look
  • 5. Sales forecasting approaches • Top Executive Approach. • Company’s Sales Representative Approach. • Customer Survey approach But….
  • 6. Regression Analysis • • • Regression analysis is used to predict the value of one variable (the dependent variable) on the basis of other variables (the independent variables). Dependent variable: denoted Y Independent variables: denoted X1, X2, …, Xk • If we only have ONE independent variable, the model is Y   0  1 x1   2 x2   3 X 3   4 x4   • Above equation referred to as simple linear regression. We would be interested in estimating β0 and β1 from the data we collect
  • 7. Basic Assumptions.. • We want to see if there is a linear relationship, i.e. • we want to see if the slope ( β ) is something other than zero. • Our research hypothesis becomes: H1: ≠ O Thus the null hypothesis becomes: H0: = O
  • 8. Data Given…
  • 9. Regression …
  • 10. Residual
  • 11. Conclusion Sales = 342 +1485.79 X1 - 0.144 X2 + 24.73 X3 Where, X1= Advertisement X2= Price X3= Housing start
  • 12. Thank you & Keep Smiling