Statistics And Probability Grade 11 Module Pdf Free Download !!TOP!!

The module is not intended to be a competitor to third-party libraries suchas NumPy, SciPy, orproprietary full-featured statistics packages aimed at professionalstatisticians such as Minitab, SAS and Matlab. It is aimed at the level ofgraphing and scientific calculators.

This is the first of three modules that will addresses the second area of statistical inference, which is hypothesis testing, in which a specific statement or hypothesis is generated about a population parameter, and sample statistics are used to assess the likelihood that the hypothesis is true. The hypothesis is based on available information and the investigator's belief about the population parameters. The process of hypothesis testing involves setting up two competing hypotheses, the null hypothesis and the alternate hypothesis. One selects a random sample (or multiple samples when there are more comparison groups), computes summary statistics and then assesses the likelihood that the sample data support the research or alternative hypothesis. Similar to estimation, the process of hypothesis testing is based on probability theory and the Central Limit Theorem.

Statistics And Probability Grade 11 Module Pdf Free Download

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First, to address the issue of scale in determining the critical value, we convert our sample data (in particular the sample mean) into a Z score. We know from the module on probability that the center of the Z distribution is zero and extreme values are those that exceed 2 or fall below -2. Z scores above 2 and below -2 represent approximately 5% of all Z values. If the observed sample mean is close to the mean specified in H0 (here m =191), then Z will be close to zero. If the observed sample mean is much larger than the mean specified in H0, then Z will be large.

This time next year, I'll be able to choose which topics I continue with, and right now I really think I would end up beelining straight towards the pure stuff and far away from the rest, but I have concerns. It seems that every mathematics-centered career I've looked at is reliant on statistics (apart from academia, but that can't really ever be more than a hope, in my opinion). Would I be seriously shooting myself in the foot if I stopped doing courses in stats/probability/etc?

While the topic may seem out of place, probability is the underlying foundation on which the important methods of inferential statistics are built. You have, more than likely, used probability. In fact, you probably have an intuitive sense of probability. Probability deals with the change of an event occurring. It is often necessary to "guess" about the outcome of an event in order to make a decision.

Politicians study polls to guess the likelihood of winning an election. Teachers choose a particular course of study based on what they think students can comprehend. Doctors choose the treatments needed for various diseases based on their assessment of likely results. You may have visited a casino where people play games chosen because of the belief the at the likelihood of winning is good. You may have chosen your pathway based on the probable availability of jobs. In the second part of this module, you will learn how to solve probability problems using a systematic approach, rather than "guessing".

Created specifically for those who are new to the study of probability, or for those who are seeking an approachable review of core concepts prior to enrolling in a college-level statistics course, Fat Chance prioritizes the development of a mathematical mode of thought over rote memorization of terms and formulae. Through highly visual lessons and guided practice, this course explores the quantitative reasoning behind probability and the cumulative nature of mathematics by tracing probability and statistics back to a foundation in the principles of counting.

In Modules 1 and 2, you will be introduced to basic counting skills that you will build upon throughout the course. In Module 3, you will apply those skills to simple problems in probability. In Modules 4 through 6, you will explore how those ideas and techniques can be adapted to answer a greater range of probability problems. Lastly, in Module 7, you will be introduced to statistics through the notion of expected value, variance, and the normal distribution. You will see how to use these ideas to approximate probabilities in situations where it is difficult to calculate their exact values.

The following assessment is comprised of 20 multiple choice and 6 extended response questions. These questions were derived from the New York State Released Test questions from NYS Assessments. Questions were also derived from Engage NY curriculum of the New York State Sixth Grade Math Module 6. This 26 question assessment provides students with a rigorous test taking opportunity. This assessment provides teachers the opportunity to assess student understanding of statistics and probability, as well as NYS standards 6.SP.1a, 6.SP.1b, 6.SP.1c, 6.SP.2, 6.SP.3, 6.SP.4, 6.SP.5, 6.SP.5a, 6.SP.5b, 6.SP.5c, 6.SP.5d, 6.SP.6, 6.SP.7, 6.SP.8, 6.SP.8a, 6.SP.8b. A student answer sheet with bubbles for multiple choice and a teacher-scored bubble section is provided. A 4-point and 2-point rubric adapted from the New York State Scoring Rubric is also provided. A testing blueprint with assessed standards is included. Teachers can use this assessment as a stand alone test or in conjunction with the New York State Module. An answer key is provided. This file is a PDF document. A Google Docs link is provided. Please be sure to make a copy of the Google Doc and save the copy in your own Google Drive. This assessment is only editable in Google Docs once you make a copy.

This module runs in Term 1 and is a core or listed optional module for some degree courses (primarily in Mathematics and Computer Science) and is also available as an unusual option to students on non-listed degrees. You may be interested in this module if you wish to take further statistics modules.

To lay the foundation for all subsequent modules in probability and statistics, by introducing the key notions of mathematical probability and developing the techniques for calculating with probabilities and expectations.

Summary module description:

This module provides an introduction to probability and probability distributions, and to fundamental techniques for statistical inference, and for the analysis of data from observational studies, with a focus on regression and hypothesis testing.

Aims:

The first half of this module provides an introduction to probability, a subject that underlies all statistical methods. Topics covered include the definition and measurement of uncertainty, the manipulation of probability statements and an introduction to both discrete and continuous probability distributions, including the role of the normal distribution. The second half of this module introduces some fundamental techniques for statistical inference, including estimation of confidence intervals and hypothesis tests. It also illustrates statistical modelling. Some simple models will be described and their role in data analysis illustrated. The use of a software package for performing the techniques will be described and illustrated.

Outline content:

Views of probability; definitions of sample spaces, outcomes and events; calculating probabilities for problems with equally likely outcomes; the axioms of probability; notions of conditional probability and independence; the law of total probability and Bayes' theorem. - An introduction to discrete random variables and their properties, including Bernoulli, binomial, negative binomial, geometric, hypergeometric and Poisson random variables. - An introduction to continuous random variables and their properties, including the uniform exponential, normal, lognormal, beta and gamma distributions. - Applications of probability, e.g. forensics, medicine, insurance, quality control and the environment. - Summary statistics, transformations and the graphical display of data. - Sampling distributions. - Confidence intervals for population means, variances and proportions in one and two samples. - Hypothesis test on one and two samples. - Categorical data analysis. Contingency tables; the chi-squared test. - The simple linear regression model; fitting a straight line; testing the significance of a regression relationship; analysis of variance.

After completing this module the student will be able to:

- apply elementary combinatorics to traditional probability problems

- compute probabilities, expectations and variances for basic probability distributions