We write, we don’t plagiarise! Every answer is different no matter how many orders we get for the same assignment. Your answer will be 100% plagiarism-free, custom written, unique and different from every other student.
I agree to receive phone calls from you at night in case of emergency
Please share your assignment brief and supporting material (if any) via email here at: [email protected] after completing this order process.
QUALIFI Level 7 Diploma in Data Science
Unit code: L/618/4971 RQFlevel: 7
AimThis unit provides learners with an in-depth understanding of statistical distribution and hypothesis testing. Statistical distributions include Binomial, Poisson, Normal, Log Normal, Exponential, T, F and Chi Square.
Parametric and non-parametric tests used in research problems are covered in this unit. The unit will help learners to formulate research hypotheses, select appropriate tests of hypothesis, write mainly R programs to perform hypothesis testing and to draw inferences using the output generated. Learners will also study planned experiments as part of the unit.
Learning Outcomes and Assessment Criteria
Learning Outcomes. When awarded credit for this unit, a learner will be able to:
Assessment Criteria. Assessment of this learning outcome will require a learner to demonstrate that they can:
1. Evaluate standard discrete and standard continuous distributions.
1.1 Analyse the statistical distribution of a discrete random variable.
1.2 Calculate probabilities using R for Binomial and Poisson Distribution.
1.3 Fit Binomial and Poisson distributions to observed data
1.4 Evaluate the properties of Normal and Log Normal distributions.
1.5 Calculate probabilities using R for normal and Log normal distributions.
1.6 Fit normal, Log normal and exponential distributions to observed data.
1.7 Evaluate the concept of sampling distribution (t, F and Chi Square).
2. Formulate research hypotheses and perform hypothesis testing.
2.1 Write R and Python programmes that evaluate appropriate hypothesis tests
2.2 Draw statistical inference using output in R
2.3 Translate research problems into statistical hypotheses
2.4 Assess the most appropriate statistical test for a hypothesis
3. Analyse the concept of variance (ANOVA) and an select an appropriate ANOVA or ANCOVA model.
3.1 Define variable, factor and level for a given research problem.
3.2 Evaluate the sources of variation, explained variation and unexplained variation.
3.3 Define a linear model for ANOVA/ANCOVA.
3.4 Confirm the validity of assumption based on definitions and analysis of variation.
3.5 Perform analysis using R and Python programs to confirm validity of assumptions.
3.6 Draw inferences from statistical analysis of the research problem.
Assessment Guidance
To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:
The quoted price covers up to 3000 words. For custom requirements Live Chat or Whatsapp ← Click Here
Email: [email protected]
Plagiarism Report
Formatting
Title page
Bibliography
Outline
Limitless Amendments
Get all these features for د.إ195.00 FREE
Check Out Our Original Reviews
