1. Analysis of Financials of ITC ltd.
Presented By:
Harshada Shewale
Shruti Mandal
Pradnya Ingle
Shreya Pandey
Imandi Venkata Deepak
Soumya Ranjan Pradhan

2. Background
We have chosen ITC Ltd. as our company of interest to analyse the trend in its financials (Sector : Cigarettes)
ITC is a diversified Indian conglomerate operating in five business segments: FMCG, Tobacco , Hotels, Stationery, Paper and Packaging and Agriculture business
In FY , ITC's revenues were INR45,102 crore and net profit of INR 7,608 Crore
Cigarettes constitute 56% of the total revenue

3. Area Focused
Analyzed Net Sales and Net Profits for the Cigarettes sector of ITC Ltd.
Data collected is from the company’s financial statements for quarters from 1997 to 2013
Net Profit = Net Sales – COGS – Operating Expenses – Non-operating Expenses + Non-operating Income – Tax
The dataset is Bi-Variate
The data is Numerical (Quantitative)

4. Analysis
We have analyzed the trends in the financial variables using statistical techniques such as descriptive statistics, Z-test, t-test and regression analysis.

5. Net Sales from operations
Analysis of Net Sales
Net Sales from operations
Mean
Standard Error
Median
Mode
#N/A
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
7450.3
Minimum
730
Maximum
8180.3
Sum
Count 65
Mean ( ) > Median ( )
The skewness ratio is 0.85 i.e. positive and close to 1. This means the data is highly right skewed, indicating sales demand is averaged around a particular value
Kurtosis is -0.3 i.e. platykurtic indicating that the sales figure has a larger degree of variation around the mean & there will be more volatility in future
Kurtosis gauges the level of fluctuation within a distribution. High levels of kurtosis represent a low level of data fluctuation, as the observations cluster about the mean. Lower values of kurtosis mean that data has a larger degree of variance.

6. Net Profit/Loss For the Period
Analysis of Net Profit
Mean (693.89) > Median (558.3)
The skewness ratio is 1.019, i.e. positive and close to 1, indicating that the data is highly right skewed.
• It indicates sales demand is averaged around a particular value
• Kurtosis is 0.114, lepokurtic, showing that it has higher peaks around the mean
• Relatively low amount of variance as return values are usually close to the mean
Net Profit/Loss For the Period
Mean
Standard Error
Median
558.3
Mode
#N/A
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
107.58
Maximum
Sum
Count 65

7. Hypothesis Test for Mean- Known Variance
We check the hypothesis mean ( ) for a random sample
Standard Deviation is given =
Two-tail test (Z-test)
Sample Mean
alpha
0.05
count
26.00 Z
-0.16
Z(alpha/2)
1.96
|Z| < Zcritical
Do not reject H0

8. Hypothesis Test for Mean- Known Variance
1. The parameter of interest is μ, the population mean
2. H0: μ =
3. H1: μ ≠
4. α = 0.05
5. The test statistic is Z-test
6. Reject H0 if Z ≥ 1.96 or if Z ≤ Note that this results from step 4, where we specified α = 0.05 and so the boundaries of the critical region are at and from normal distribution tables
7. Calculated Z:
Z = ( )/ ( /sqrt26)= -0.16
8. Inference:
As |Z| < Z(alpha/2) i.e <1.96
Do not reject H0, i.e. the mean sales does not differ from last year

9. Hypothesis Test for mean Unknown Variance
To check whether H 1 sales is greater than 3,027 or not?
We employ a t-test
t-Test
Sample Mean
4,852.70
alpha
0.05
count 30 t
6.34
T (alpha d.f.)
2.045
Hypothesized mean is greater than
Reject H0

10. Hypothesis Test for Mean- Unknown Variance
1. The parameter of interest is μ, the population mean
2. H0: μ =
3. H1: μ ≠
4. α = 0.05
5. The test statistic is t-test
6. Reject H0 if t ≥ Note that this results from step 4, where we specified α = 0.05 and so the boundaries of the critical region are at and from normal distribution tables
7. Calculated t:
t = ( )/ )= 6.34
8. Inference:
As t > t critical
Reject H0, i.e. the mean sales does differ from last year

11. Hypothesis Testing 2 Sample Test
To check whether mean of H1 & H2 is equal or not?
Two samples are taken and we assume that both have the same mean
t-Test: Two-Sample Assuming Equal Variances of population
t-Test: Two-Sample Assuming Equal Variances
Variable 1
Variable 2
Mean
Variance
Observations 20
Pooled Variance
Hypothesized Mean Difference df 38
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
2 tail test
H0 Hypothesis mean1=mean2
alpha
0.05
H0 is not rejected
t stat < t critical
p>alpha

13. Correlation & Regression

14. Thank You