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# Introduction to Statistical Analysis

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### Introduction to Statistical Analysis

1. 1. Data Analysis CourseBasics & Terminology(Version-1) Venkat Reddy
2. 2. Data Analysis Course• Data analysis design document•• Descriptive statistics• Data exploration, validation & sanitization 2 Venkat Reddy Data Analysis Course• Probability distributions examples and applications• Simple correlation and regression analysis• Multiple liner regression analysis• Logistic regression analysis• Testing of hypothesis• Clustering and decision trees• Time series analysis and forecasting• Credit Risk Model building-1• Credit Risk Model building-2
3. 3. Note• This presentation is just class notes. The course notes for Data Analysis Training is by written by me, as an aid for myself.• The best way to treat this is as a high-level summary; the actual session went more in depth and contained other 3 Venkat Reddy Data Analysis Course information.• Most of this material was written as informal notes, not intended for publication• Please send questions/comments/corrections to venkat@trenwiseanalytics.com or 21.venkat@gmail.com• Please check my website for latest version of this document -Venkat Reddy
4. 4. What is “Statistics”?• Statistics is the science of data that involves: • Collecting • Classifying • Summarizing • Organizing and Venkat Reddy Data Analysis Course • InterpretationOf numerical information.• Examples: • Cricket batting averages • Stock price • Climatology data such as rainfall amounts, average temperatures • Marketing information 4 • Gambling?
5. 5. Key Terms• What is Data? • facts or information that is relevant or appropriate to a decision maker• Population? • the totality of objects under consideration Venkat Reddy Data Analysis Course• Sample? • a portion of the population that is selected for analysis• Parameter? • a summary measure (e.g., mean) that is computed to describe a characteristic of the population• Statistic? • a summary measure (e.g., mean) that is computed to describe a characteristic of the sample 5
6. 6. Variables• Traits or characteristics that can change values from case to case.• Examples: • Age Venkat Reddy Data Analysis Course • Gender • Income • Social class 6
7. 7. Types Of Variables• In causal relationships: CAUSE  EFFECT independent variable  dependent variable• Independent variable: is a variable that can be controlled or Venkat Reddy Data Analysis Course manipulated.• Dependent variable: is a variable that cannot be controlled or manipulated. Its values are predicted from the independent variable.• Discrete variables are measured in units that cannot be subdivided. Example: Number of children• Continuous variables are measured in a unit that can be subdivided infinitely. Example: Height 7
8. 8. Lab• Print product sales data• What are cause variables, what are effect variables• Identify the continuous & discrete variables• What is the population Venkat Reddy Data Analysis Course• Filter data and pick a sample• Calculate a parameter (Mean of the population)• Calculate a statistic• How close is the statistics to parameter? Is it a good estimate?• Self study: Randomly pick 10 samples, calculate mean for each sample. Find the mean of the means & see whether it is a good estimate of the population mean 8
9. 9. Descriptive Statistics• Gives us the overall picture about data• Presents data in the form of tables, charts and graphs• Includes summary data• Avoids inferences Venkat Reddy Data Analysis Course• Examples: • Measures of central location • Mean, median, mode and midrange • Measures of Variation • Variance, Standard Deviation, z-scores 9Details later
10. 10. Lab• Download product sales data• Run proc means to print the descriptive statistics• Run proc univariate to print the descriptive statistics• Identify Measures of central location Venkat Reddy Data Analysis Course• Identify Measures of variation 10
11. 11. Inferential Statistics• Take decision on overall population using a sample• “Sampled” data are incomplete but can still be representative of the population• Permits the making of generalizations (inferences) about the Venkat Reddy Data Analysis Course data• Probability theory is a major tool used to analyze sampled data-Details later 11
12. 12. Predictive Modeling• The science of predicting future outcomes based on historical events.• Model Building: “Developing set of equations or mathematical formulation to forecast future behaviors based on current or historical data.” Venkat Reddy Data Analysis Course• Regression, logistic Regression, time series analysis etc., 12-Details later
13. 13. Statistical Computer Packages Typical Software • SAS • R • SPSS • MINITAB • Excel
14. 14. Venkat Reddy KonasaniManager at Trendwise Analyticsvenkat@TrendwiseAnalytics.com 1421.venkat@gmail.com Venkat Reddy Data Analysis Course+91 9886 768879