USES OF ANALYTICS IN BUSINESS
1
OUTLINE
¡ Evolution of analytics/reports
¡ Basic Statistics
¡ Statistics using Excel
2
THE EVOLUTION
¡ Classic Analytics (Old School Reports)
¡ Manual data collection -> Reports
¡ Pros – Simplistic – Record Keeping – Consistency – Apt for the use
¡ Cons – Time consuming – Expensive – Prone to human error – Limited Scope - Lagged
¡ Modern Analytics (Dashboard)
¡ Automated data collection -> Automated Reports – Dashboards – Analytics
¡ Pros – Real time (Almost) –Integrated (not always) – Less chances of errors – Expandable Scope -
High value creation
¡ Cons – Costly – Need for Infrastructure and resources – Unknown Risks – Greater need for
oversight (Beta versions)
USE OF ANALYTICS BY FIELDS AND INDUSTRY
¡ Financial Institutions/Services
¡ Traditional FI’s
¡ Fintechs
¡ Payroll Accounting
¡ Social Media
¡ Energy
¡ Sports
¡ Construction
¡ Economics
WHAT ARE YOU LOOKING FOR IN THE DATA?
Patterns or Trends
¡ Cyclical: cycle occurs when the data exhibit rises and falls that are not of a fixed frequency
¡ Upward: increasing trend
¡ Downward: decreasing trend
¡ No pattern or trend: random fluctuations which do not appear to be very predictable, and no
strong patterns that would help with developing a forecasting model.
WHAT ARE YOU LOOKING FOR IN THE DATA?
Source otexts website
WHAT ARE YOU LOOKING FOR IN THE DATA?
¡ Outliers – Strange or a rare event/data
¡ Decision to include in data
¡ 1) If the occurrence is real, if yes split into two:
a) with outlier b) without outlier
¡ 2) If the not real, exclude it
Source datascience website
STATISTICS
¡ Central Tendencies
¡ Average (Mean): A number expressing the central or typical value in a set of data. (can be
effected significantly by the outliers).
¡ Median: The value separating the higher half from the lower half of a data sample
(not effected significantly by the outliers).
¡ Mode: most repeated number or any qualitative data.
Source wikihow website
STATISTICS
¡ Central Tendencies
¡ Standard Deviation (Std Dev.) – Fluctuations or volatility. Measures deviations away from
mean.
¡ Coefficient of Variance (CoV) =
!"# $%&.
(&%)*+%.
¡ Comparable Measure of volatility.
¡ Use it to determine whether the expected return
of the investment is worth the degree of volatility.
¡ A lower ratio suggests a more favorable tradeoff
between risk and return.
Source Wikipedia org
BASIC STATISTICS
¡ Average
¡ Simple – Sum of observations or data / number of observations or data points
¡ Weighted – Average with different importance to different observations
Example 1:
Example 2:
Assignment
Score (100)
Course Weight
Weighted score
Participation
100
0.1
10
Individual paper 1
85
0.2
17
Individual paper 2
70
0.2
14
Assignment
100
0.1
10
Term Project
95
0.2
19
Final Exam
90
0.2
18
Average
90
88
WWAC: Weighted average cost of capital (weighted average cost of firm’s cost of equity and dept)
BASIC STATISTICS
¡ Average
330
¡ Moving Average: Averages that move with
certain time frames. For example, 3 months
moving average in May = average of February,
March and April.
Number of Customers
320
310
¡ A simple forecast technique for time series data
300
(Can be assessed using MAE, MAPE, etc.)
290
280
270
0
5
10
15
Days
20
25
*Follow the excel in the class
CORRELATION
¡ Correlation (r) – explains co movement of the two variables that are interdependent. (x and y)
¡ Does: How two interdependent variables move.
¡ Doesn’t: If one is causing the change in another.
¡ Example: Inflation and Interest rate. Unemployment and Inflation.
¡ Perfect Positive (r=1) – Movement is in the same direction as well as proportion.
¡ Imperfect positive (r >0 - <1) – Movement is in the same direction but in different proportion.
¡ No correlation (r=0) – There is no or little correlation.
¡ Perfect Negative (r=-1) – Movement is in opposite direction but in the same proportion.
¡ Imperfect Negative (r <0 - >-1) Movement is in opposite direction but in the different proportion.
CORRELATION
¡ Correlation (r)
Source © 2018 ThoughtCo Wikipedia
REGRESSION
¡ Regression is a linear relationship between two variables. One variable is independent, another is
dependent. y is dependent on x.
¡ Regression measures changes in dependent variable (y) given the change in independent variable (x).
Remember: Independent moves by itself.
¡ Independent: Explanatory variable. Dependent: Explained variable.
¡ Regression model: y = Alpha + Beta (x)
¡ Intercept: Alpha. What y equals to Alpha when x (or all independent variables) is (are) zero.
¡ Slope: Beta.
¡ Model Interpretation: How the independent variable explains the change in the dependent variable.
¡ Single Factor: Only one dependent and one independent variable.
¡ y = Alpha + Beta1 (x1)
¡ Multiple Factor: Only one dependent and more than one independent variable.
¡ y = Alpha + Beta1 (x1) + Beta2 (x2)
HOW TO INTERPRET REGRESSION RESULTS?
¡ R Square: Fitness of the model. Combined % of change in the dependent variable explained by all the
independent variables. Or. How much % change in the dependent variable is explained by all or one
independent variable.
¡ Range: 0 to 1. What if the R Square is equal to 0.99? Spurious regression.
¡ Adjusted R Square: Is R Square adjusted for errors or noise in the data. Too much difference
between R Sqr and Adj. R Sqr means more noise or error in the data.
¡ Intercept: When one more all or one the independent variables are equal to zero. Dependent
variable = Intercept (Alpha).
¡ Slope: If the independent variable increases by 1 (unit of measurement), the dependent variable will
increase or decrease by the slope (unit of measurement). Keeping all the other factors constant.
¡ t Stat: For Statistical significance. Refer to the table or compare it to 2.
¡ Multi-colinearity: When two independent variables are correlated (r > + or -0.75).
HOW TO INTERPRET REGRESSION RESULTS?
¡ We cannot use 𝑅 , for model comparison when the competing models do not include the same
number of predictor variables (but have the same response).
¡ 𝑅 ! never decreases as we add more variables.
¡ May include variables with no economic or intuitive foundation.
¡ Adjusted 𝑅 , explicitly accounts for the sample size 𝑛 and the number of predictor variables 𝑘.
¡ Imposes a penalty for any additional predictors.
¡ The higher the adjusted 𝑅 ! , the better the model.
¡ When comparing models with the same response, the model with the higher adjusted 𝑅 , is preferred.
EXAMPLES
¡ Correlation
¡ Inflation – Unemployment
¡ Regression
¡ In class example – Average prices in Vancouver (explained) – Variables explaining the change in explained
variable (explanatory variable)
¡ Anova Table – Interpret – R² - Adjusted R² - Intercept – Coefficients – T – Statistics
CAUTION!
¡ Correlation and Regression do not take into account the qualitative side of the data.
¡ So, high correlation between ice cream sales and crime rate means, people who eat more ice creams commit
more crimes?
¡ Answer:
¡ Understand the model or correlation you run, better numbers don’t always mean the right results!
Thank you!
Questions?
19