Slide strategic financial management l02

Comparing Standard Deviations Mean = 15.5 S = 3.338  The smaller the standard deviation, the more tightly clustered the scores around mean  The larger the standard deviation, the more

STRATEGIC FINANCIAL MANAGEMENT

BASIC STATISTICS

KHURAM RAZA

First Principle and Big Picture

Summarizing Data

The problem that we face today is not that we have too little information but too much Making sense of large and often contradictory information is part of what we are called upon to do when analyzing companies.

 What values occur most frequently and

 The range of high and low values.

"central" value of a set of numbers

To calculate: Just add up all the numbers, then divide

by how many numbers there are.

Add the numbers: 2 + 7 + 9 = 18

So the Mean is 6

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Variance & Standard deviation

Variance & Standard deviation

Mr X has eight eggs Each egg was weighed and recorded as follows:

Comparing Standard Deviations

Mean = 15.5

S = 3.338

 The smaller the standard deviation, the more tightly clustered the scores around mean

 The larger the standard deviation, the more spread out the scores from mean

Coefficient of Variation (CV)

Can be used to compare two or more sets

of data measured in different units or same units but different average size.

Use of Coefficient of Variation

Both stocks have the same standard deviation

Standardized Variable

The industry in which sales rep Mr Atif works has mean annual

sales=$2,500 standard deviation=$500

The industry in which sales rep Mr Asad works has mean annual

sales=$4,800 standard deviation=$600

Last year Mr Atif’s sales were $4,000 and Mr

Asad’s sales were $6,000.

Performance evaluation by z-scores

Which of the representatives would you hire if

you have one sales position to fill?

02:57:51 PM

Performance evaluation by z-scores

Sales rep Atif

500 ,

2 000

Z

S

X

X Z

2 600

800 ,

4 000

Z

S

X X

Z

100 200 300 400 500 600 700 800 900 1000 0

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Sales COGS Selling Exp Admin Exp

Relationships in the Data

When there are two series of data, there are a number

of statistical measures that can be used to capture how the two series move together over time

Covariance

Correlations

Regressions

Covariance indicates how two variables are related A positive covariance means the variables are positively related, while a negative covariance means the variables are inversely related The formula for calculating covariance of sample data is shown below.

The covariance between the returns

of the S&P 500 and economic growth is

1.53 Since the covariance is positive,

the variables are positively related—they

move together in the same direction.

addition to telling you whether variables are positively or inversely related, correlation also tells you the degree to which the variables tend to move together.

take on a value between 1 and – 1:

 If the correlation coefficient is one, the variables have a perfect positive correlation.

 If correlation coefficient is zero, no relationship exists between the variables.

 If correlation coefficient is –1, the variables are perfectly negatively correlated (or

inversely correlated).

A correlation coefficient of 66 tells

you two important things:

 Because the correlation coefficient is a positive number, returns on the S&P 500 and economic growth are positively related.

 Because 66 is relatively far from indicating no correlation, the strength

of the correlation between returns on the S&P 500 and economic growth is strong

Y = a + b X

Slope of the Regression

Intercept of the Regression

Regressions