This document discusses correlation and regression analysis. It defines correlation as the statistical technique used to determine how strongly two variables are related. Correlation can be positive if the variables increase together, or negative if one variable decreases as the other increases. Regression analysis models the relationship between a dependent variable and one or more independent variables. Regression equations and different types of regression models like simple linear and non-linear regression are presented. Examples of applying regression to forecast sales trends over time and determine the effect of price changes on demand are provided.

1. ASSIGNED BY: SIR ABDUL SALAAM
Co relation & Regression
Presented By : Reihan ali Kango
Mahwish Shaikh
Statistics

2. content
 Co-relation
 Definition
 Graph
 Problem
 Regression
 Regression Model
 Equation
 Applications
 Problem

3. The word Correlation is made of Co- (meaning "together"), and Relation
Correlation is a statistical technique used to determine the degree to
which two variables are related
Co-relation

4. Correlation is Positive when the values increase together, and
Correlation is Negative when one value decreases as the other
increases

5. Ice Cream Sales vs Temperature
Temperature °C Ice Cream Sales
14.2° $215
16.4° $325
11.9° $185
15.2° $332
18.5° $406
22.1° $522
19.4° $412
25.1° $614
23.4° $544
18.1° $421
22.6° $445
17.2° $408

6. X Y Xy X2 y2
8 13 104 64 169
9 14 126 81 196
10 16 160 100 256
7 11 77 49 121
6 11 66 36 121
8 14 112 64 196
6 12 72 36 144
6 10 60 36 100
60 101 777 466 1303
Problem On board

7. In statistics, regression analysis is a statistical process for
estimating the relationships among variables.
It includes many techniques for modeling and analyzing several
variables,
when the focus is on the relationship between a dependent
variable and one or more independent variables.
Regression

8. Regression Equation

9. Regression
simple
linear
Non linear
Regression Model

10. Applications
 If a company's sales have increased steadily every month for the past few years,
conducting a linear analysis on the sales data with monthly sales on the y-axis
and time on the x-axis would produce a line that that depicts the upward trend in
sales. After creating the trend line, the company could use the slope of the line to
forecast sales in future months.
 The effect of a price increase upon demand, for example, or the effect of
changes in the money supply upon the inflation rate.

11. Problem On board
X Y Xy x2
42 5 210 1764
27 10 270 729
36 8 288 1296
25 12 300 625
22 13 286 484
39 7 273 1521
47 7 329 2209
19 11 209 361
33 9 297 1089
30 8 240 900
320 90 2702 10978

12. Any Questions ?
Thank you..