Inferential statistics examples problems

And draw conclusions based on the sample. There are two main types of statistics - Descriptive Statistics and Inferential Statistics.

Make sure the sample size is big enough to model differences with a normal population.

Inferential statistics examples problems. A few sample problems for inferential statistics Problems. Suppose X 1X 100 are iid random variables which have uniform dis-tribution on a 2a2 where ais unknown. Suppose the random sample produces sample mean equal to 3.

Compute a 95 con dence interval for a. In a mythical national survey 225 students are randomly selected from. Inferential Statistics Examples.

There are lots of examples of applications and the application of inferential statistics in life. However in general the inferential statistics that are often used are. Regression analysis is one of the most popular analysis tools.

And draw conclusions based on the sample. Inferential statistics is all about relationships and quantitative analysis. You can use inferential statistics to create logistic regression analysis and linear regression analysis.

Descriptive Statistics Descriptive statistics describe and summarize data. Examples include numerical measures like averages and. Yes it is possible to use statistics inferential without generalising your results.

This is mostly common in situations where data were collected from samples that werent selected randomly. TESTS FOR INFERENTIAL STATISTICS T-Test Can be used as an inferential method to compare the mean of the sample to the population mean using z-scores and the normal probability curve. You use t-curves for various degrees of freedom associated with your data.

Degrees of freedom are the number of observations that vary around a constant. Techniques that allow us to make inferences about a population based on data that we gather from a sample. Study results will vary from sample to sample strictly due to random chance ie sampling error.

Inferential statistics allow us to determine how likely it is. Inferential statistics allows you to make predictions inferences from that data. With inferential statistics you take data from samples and make generalizations about a population.

For example you might stand in a mall and ask a sample of 100 people if they like shopping at Sears. You could make a bar chart of yes or no answers that would be descriptive statistics or you. Inferential statistics is a technique used to draw conclusions and trends about a large population based on a sample taken from it.

For example lets say you need to know the average weight of all the women in a city with a population of million people. It isnt easy to get the weight of each woman. The solution involves four steps.

Make sure the sample size is big enough to model differences with a normal population. Because n 1 P 1 100 052 52 n 1 1 - P 1 100 048 48 n 2 P 2 100 047 47 and n 2 1 - P 2 100 053 53 are each greater than 10 the sample size is large enough. Statistics is a broad subject that branches off into several categories.

In particular Inferential Statistics contains two central topics. Estimation theory and hypothesis testing. The goal of estimation theory is to arrive at an estimator of a parameter that can be implemented into ones research.

Apply inferential statistics to problems Understand why you would use descriptive statistics Know what hypothesis testing correlation testing and regression analysis are. This study aimed to describe forms of erro rs m ade by students in solving inferential. Statistics problems based on Newman analysis stages.

The results showed that there were 4 stages. Inferential statistics frequently involves estimation ie guessing the characteristics of a population from a sample of the population and hypothesis testing ie finding evidence for or against an explanation or theory. Statistics describe and analyze variables.

We discuss measures and variables in greater detail in Chapter 4. A variable is a measured characteristic or attribute that may assume different values. Knowledge of statistics ie only took an introductory statistics course or no knowledge of statistics ie never took a statistics course represented inferential statistics problems.

They examined students problem representations after a session in which the students were exposed to examples. There are two main types of statistics - Descriptive Statistics and Inferential Statistics. Descriptive Statistics consists of organizing and summarizing data.

Inferential Statistics consists of using data you have measured to form conclusions. Population is the group you are interested in studying. In inferential statistics we use sample statistics to estimate population parameters.

For example if we collect a random sample of adult women in the United States and measure their heights we can calculate the sample mean and use it as an unbiased estimate of the population mean. Lets look at an example of an inferential statistic word problem 74. Skating rink officials want to know if teenagers in PoDunk Town prefer rink skating better than park skate boarding.

They ask a sample of teenagers and record their responses. For instance your sample mean is unlikely to equal the population mean exactly. The difference between the sample statistic and the population value is the sampling error.

Inferential statistics incorporate estimates of this error into the statistical results. In contrast summary values in.