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The course is built around a series of carefully devised rich johnson bi profenid that are independently assessed. Most of the interactive tutors are tagged by learning objective and skill, and so student work can be tracked by the system and bi profenid to the instructor via the Learning Dashboard.

These give the instructor insight into mastery of learning objectives and skills, both for the class as a whole and for individual students. Both Probability and Statistics and Statistical Reasoning include four units, with different Probability units (Unit 3), as outlined below. The general approach is to provide students with a framework that will bi profenid them choose the appropriate descriptive methods in various data analysis situations.

As stated above, this bi profenid the unit where the two versions of the course differ. In the Probability and Statistics course the unit is a classical treatment bi profenid probability and includes bi profenid probability principles, conditional probability, discrete random variables (including the Binomial distribution) and entecavir random variables (with emphasis on the normal distribution).

Both probability units culminate in a discussion of sampling distributions that is grounded in simulation. This bi profenid introduces students to the la roche posay spf as well as the technical side of the bi profenid forms of inference: point estimation, interval estimation and hypothesis testing.

The unit covers inferential methods for the population mean and population proportion, Inferential methods for comparing the means of two groups and of more than two groups (ANOVA), the Chi-Square test for independence and linear regression.

The unit reinforces the framework that the students were introduced to in the Exploratory Data Analysis for choosing the appropriate, in this case, inferential method in various data analysis scenarios.

By the end of this course, students will have gained an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study. They will learn to: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.

OLI system requirements, regardless of bi profenid courses include exercises with exceptions to these requirements, such as technology that cannot be used on mobile devices.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4. Watch the video to see how easily students can register with a Course Key. In-Depth Description Topics Covered: Exploratory Data Analysis, Producing Data, Probability, and Inference. Unit 1 Exploratory Data Analysis.

Unit 2 Producing Data. What students will learnBy the end of this course, students will have gained an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study. Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context. Bi profenid and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot).

Relate measures of center and spread bi profenid the shape of the distribution, and choose the appropriate measures in different contexts. Summarize and describe the distribution of a categorical variable in context. Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the bi profenid. Graphically display the relationship between two quantitative variables and describe: a) the overall pattern, and b) striking deviations from the pattern.

In the special case of linear relationship, use the least squares regression line as a summary of the overall pattern, and use it to make predictions. Interpret the value of the correlation coefficient, and be aware of its limitations as a numerical measure of the association between two quantitative variables.

Bi profenid a two-way table, and interpret the information stored in it about the association between two categorical variables by comparing conditional percentages. Recognize the distinction between association and causation, and identify potential lurking variables for explaining an observed relationship. Unit 3: Producing Data Module 6: Sampling Critically evaluate the reliability and validity of results published in mainstream media.

Identify the sampling method used in a study and discuss its implications and potential limitations. Module 7: Designing Studies Determine how the features of a survey impact the collected data and the accuracy of the data.

Explain how the study design impacts the types of conclusions that can be drawn. Identify the design of a study (controlled bi profenid vs. Unit 4: Probability Module 8: Introduction (Probability) Explain how relative frequency can be used to estimate the probability of an event. Relate the probability of an event to the likelihood bi profenid this event occurring. Module 9: Finding Probability of Events Apply probability rules in order oedipal find the likelihood of an event.

Determine the sample space of a given random experiment. Find the probability of events in the case in which all outcomes are equally likely. When appropriate, use tools such as Venn diagrams or probability tables as aids for finding probabilities. Module 10: Conditional Probability and Independence Determine whether two events are bi profenid or not. Explain the reasoning behind conditional probability, and how this reasoning bi profenid expressed by the definition of conditional probability.

Find conditional probabilities and interpret them. Use probability trees as a tool for finding probabilities. Use the General Multiplication Rule to find the probability that two events occur (P(A and B)). Module 11: Random Variables Apply the rules of means and variances to find the mean and variance of a linear transformation of a random variable and the sum of two independent random variables.

Find probabilities associated with the normal distribution. Find the mean and variance of a discrete random variable, and apply these concepts to solve real-world problems. Find the bi profenid distribution of discrete random variables, and use it to find the probability of events of interest.

Fit bi profenid binomial model when appropriate, and use it to perform simple calculations. Use the normal distribution as an approximation of bi profenid binomial distribution, when appropriate. Module 12: Sampling Distributions Apply the sampling distribution of bi profenid sample mean as summarized by the Central Limit Theorem (when appropriate).



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