About Statistical Analysis Certification Training
Do you have these questions playing on your mind?
Should I open a new store for my business? Should I launch new product? Should I renew contract of my vendor? Whom should I give more bonuses among my team members? Who will win cricket world cup? Who will win elections?
AND how many times have you gone wrong with such decision making?
- Answer will be an obvious – “I was wrong Many Times” if the decision making is based on intuition.
- Answer will be an obvious – “I was right Many Times” if the decision making is based on statistical analysis.
- Statistical analysis is used to take decisions across industries and across job levels. Intuition based decision making should be coupled with a factor called LUCK, for success. Most companies have not even seen the light because of lack of statistical analysis. We at ExcelR expose you to various statistical techniques for data analysis using a tool called “R/RStudio”. Revolution Analytics was recently acquired by Microsoft but will still continue to be an open source statistical software. With the tremendous increase in data generation which is set to increase by 4 thousand times by 2020 brings with it a new challenge on how to analyze the data faster to give business leaders insights for decision making. This is where statistical analysis comes in handy which forms the spine strength for any data scientist. Rather than trusting the gut feel, a data scientist uses statistical data analysis to bring in business value.
Things You Will Learn…
Random Variables, Probability Distributions
• Motivate the use of statistical methods for managerial decision making
• Discuss the concepts of probability distributions and random variables
• Review methods of representing data, pictorially and through summary statistics
Properties of Normal Distribution
• Introduce standard normal distribution
• Discuss applications of normal distribution
Sampling Distributions and the Central Limit Theorem
• Introduce the concept of statistical inference
• Recognize the existence of sample-to-sample variations
• Understand central limit theorem and its implications for statistical inference
Confidence Intervals (I)
• Introduce the concept of confidence intervals as a way to make statistical inferences
• Calculate confidence intervals for population mean with known and unknown population standard deviations
Confidence Intervals (II)
• Calculate confidence intervals for population proportions
• Calculate confidence intervals for population variance
• Quantify minimum sample sizes to achieve certain margin of error in predictions
Hypothesis Tests (I)
• Learn how to state null and alternative hypotheses
• Understand type-I and type-II errors
• Conduct one-sided hypothesis test for population proportion / mean
Hypothesis Tests (II)
• Conduct two-sided hypothesis tests for population proportion / mean
comparison of Two Populations
• Compare the means using paired observations
• Test for the difference of two population means using independent samples
• Test for the difference of two population proportions
Analysis of Variance
• Introduce Design of Experiments
• Conduct one way Analysis of Variance (ANOVA)
• Introduce the notion of statistical tests on ordinal data
• Test for the difference between mean ranks using paired observations
• Compare mean ranks in two independent samples
Basics of & Advanced Regression Methods
• Bivariate data; Scatter plot; Covariance; Correlation coefficient; Uses and issues; Correlation and causality; Linear regression; Assumptions
• Several regressors; Scatter plot matrix; Multiple linear regression; Assumptions; Ordinary Least Squares method (OLS); Basic regression summary; Interpretation of coefficient estimates, standard errors, t-values and p-values, and adjusted; ANOVA table; Basic tests
• Anscombe’s data sets; Need for deeper analysis; Residuals; Deletion diagnostics; Added variable plots; Partial correlation; Model adequacy checks; Plots– Fitted values vs Residuals, Regressors vs Residuals, Normal probability plot
• Problem of insignificance of important regressors – Collinearity; Detection – correlation matrix, VIF, variance proportions table; Remedies; subset selection, best subset; Criteria – R2, Adjusted R2, AIC, BIC
• Ridge regression; Dummy variables; Transformations – Power transformation, Box-Cox transformation
• Heteroscadasticity; Possible causes; Detection – graphical methods, formal tests; Remedies – Transformations, Adjustment to standard errors of OLS estimates, Generalized least squares
• Autocorrelation; Possible causes; Detection – graphical methods, formal tests; Remedies – First differences, Adjustment to standard errors of OLSestimates, Generalized least squares, Dummy variables and autocorrelation, forecasting in the presence of autocorrelation
• Binary response; Linear Probability Model; Advantages and issues; Guidelines for Linear Regression Modeling
Regression Models for count data
• Generalized Linear Models
• Binary and multinomial logistic regressions
• Poisson regression
• Zero-inflated Poisson regression
• Negative Binomial regression
Missing Value Analysis
• Missing value patterns: Missing completely at random (MCAR). Missing at random (MAR). Missing not at random (MNAR)
• Listwise deletion. Pairwise deletion
• Various imputation methods: Hot deck imputation. Mean substitution. Regression imputation. EM imputation
• Censoring and truncation. Characteristics of survival analysis data
• Time-to-event data. Hazard and survival functions
• Kaplan-Meier estimate of survival function
• Cox proportional hazards model (ph), estimation and its analysis. Extensions
• Stratified ph; ph with time-varying covariates
• Parametric survival analysis with standard distributions
• Accelerated failure time models
Design of Experiments
• Basic concepts: randomization, replication and control
• Experimental design for testing differences in several means: Completely randomized and randomized complete block designs. Cross-over designs
• Two-level factorial experiments—full and fractional. Plackett-Burman designs
• Designs for three or more levels. Taguchi designs. Response surface designs
• Case-Control designs for campaign evaluation
• Designs for conjoint analysis
Who Should do Statistical Analysis Training
Anyone who has business acumen with an interest and passion towards predicting the future using statistics. Given that the boom for this professional has started very recently, every fresh graduate can see to this profession as an easy way to get a job & pursue the most sorts after profession in the world.
People who are in Data Warehouse & Business Intelligence can pursue Statistical Analysis Certification Training as a next logical move in their careers. A few other professions which might want to pursue this course include:
- Data scientists
- Data analysts
- Business analysts
- Six Sigma consultants
- CMMI consultants
- Process consultants
- Big Data developers / administrators
- Software developers
- Fresh graduates, etc.
Statistical Analysis Course Introduction Video
- We don’t wash off our hands after the completion of the training. A dedicated trainer will be provided as mentor to guide you subsequent to the training. He will do the necessary handholding to clarify all your doubts. In addition you can approach our support team to answer your queries. We will be more than happy to assist you.
- Faculty is our strength. All our instructors are professionals with 10-15 years of working experience in various domains. We handpick the trainers, who are experienced, have passion for training and who possess excellent training skills. All our trainers are considered to be the best faculty of the industry.
- Yes. ExcelR is one of the leaders in Training. We have tied up with many Indian and Multinational companies. They contact us for student profiles as and when the vacancies exist. We connect the eligible students to the prospective employers. We help in preparing resume, provide assistance for interview preparation, real time projects etc. We provide support till the student is placed.
- Yes, the participants will be provided access to our state-of-the-art Learning Management System (LMS) the moment they are registered for the course. They access the session online and gain some insight towards the subject, before attending a live class
- ExcelR conducts periodical demo sessions on all the courses. Request a demo by Registering in the website. We shall share the demo details.
- Send us an email email@example.com with your query. Our Subject Matter Experts/Sales team will approach you to clarify your queries or call us on 1800-212-2120 (Toll Free number – India), 608-218-3798 (USA), 800 800 9706 (India), 203-514-6638 (United Kingdom), 128-520-3240 (Australia).
- There is no prerequisite for this program. Course takes participants right from basics to advanced concepts. For e.g. what is an average/mean until advanced regression topics including Linear, Logistic, Poisson regression etc.
1) E-learning modules are available on our state-of-the-art LMS. You can access the videos at your convenience
2) All online sessions will be recorded and uploaded in a centralized repository where you can access the classes you missed and join the next session without hassles.