PSPP can perform the following statistical analysis;
- ROC curves
- Linear regression
- Chi square and Binomial tests
- One way ANOVA
- Descriptive statistics
- T Tests
PSPP has a compute variable window for creating new variable by applying mathematical functions. It is considered a replacement for SPSS because its backend is designed to perform analyses as fast as possible, regardless of the amount of input data. PSPP can support 1 billion data observation, use SPPS files and is compatible with Open Office.
Some of the exciting features of PSPP include;
- It provides its output on PDF, Postscript, SVG, ASCII or HTML formats.
- PSPP accepts data in spreadsheets, OpenDocument, Postgres etc.
- Some libraries used by PSPP can be accessed.
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PSPP Homework Help
PSPP was first developed at the end of the 90s and produced by SPSS Inc. It does not require additional purchases to get advanced functions. PSPP is a very reliable tool that can be used to perform Cluster Analysis, Non-parametric tests, T-tests, logistic regression and ANOVA. The user can either use the graphical user interface or the command line while performing analysis.
For the students to grasp knowledge and know how to use the software, the instructors always give out PSPP homework. The more the assignments the more the student understands how it is used. These assignments may limit the students to studying and not having a social life. We know students need time for themselves and do some part time jobs to earn income.
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Chi square is a statistical tool that measures the comparisons between expectations and results. It requires that the data used must be raw, random, mutually exclusive, drawn from large enough sample and from independent variables. The Chi square shows any inconsistency that may exist between the expected results and the actual results.
Chi square applies the Sum of Squares technique which is used in regression analysis. The technique can be used to determine the dispersion of data points. This may help in explaining how a data series was generated. The sum of squares measures the deviation from the mean.
To understand the importance of the Chi square statistical tool and the validity of the null hypothesis, it is essential to calculate the degrees of freedom. Degrees of freedom are the number of values in a study that have the freedom to vary. Chi square has two different kind of important test, they are;
- Test of independence- This test tries to find out the relationship between the variables.
- Goodness of fit Test- This type of test sums up the differences between observed and expected outcome frequencies.
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Statistics is an advanced branch of mathematics that deals with the collection, analysis, interpretation, presentation and organization of sampled data. It involves the use of formulas and sophisticated software to manipulate data. Statistics is applied in many disciplines that use large volumes of data like business, government, and manufacturing. To analyse data, a researcher can either use a descriptive statistics method or the inferential statistics method.
Statistics is a very vast subject and it covers a lot of topics such as Kurtosis, Analysis of Variance, skewness, and regression analysis. In statistics, it is very important to know the statistical significance of data. The statistical significance implies that the result from a test or experiment has a specific cause.
Students doing statistics course therefore need to be very sharp and alert because most of the topics are interrelated. The wrong use of formulas or data means wrong results and a fail in exams or assignments. Statistics has given many students great stress and headache. Most students are always forced to look for experts to help them understand the concepts and theories involved.
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Analysis of variance is a statistical tool that performs tests on the various factors that affect a given data set. ANOVA is mostly applied on experimental data. ANOVA spreads out the variance into diverse sources by grouping differences and comparing the means of each group. There are two main types of ANOVA analysis;
- One-way ANOVA- It is also known as unidirectional. It only has one independent variable. It evaluates the impact of the factors on a sole response variable.
- Two-way ANOVA- This type of ANOVA has two independent variables. A researcher can use this method to observe the interaction between the two factors at the same time.
ANOVA was created in 1918 by Ronald Fisher. It is an extension of the t-test and z-tests. It can be used to determine whether two or more groups have different means. Most of the tests in ANOVA like the Balanced ANOVA test are conducted by software because they are complex and require a lot of mathematical calculation. Most corporations and governments use the Budget Variance in ANOVA to quantify the difference between budgeted and actual figures for a certain period.
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Inferential statistics is a tool used to infer from a sample data. It is used with descriptive statistics to provide a very powerful tool for description and forecasting. It is impractical to try and get the exact data available in the population of study; this is where inferential statistics come into play. Instead of using data from the entire population, the researcher will choose a sample. The sample is drawn from the population and will represent the entire population. The data obtained from the sample is used to make generalization about the population.
Inferential statistics covers the following two main areas;
Parameters estimation- This means that a statistic is taken from the sample drawn from the population and is used to make judgements about the entire population
Hypothesis testing- This is where the sample data is used to answer research questions, for example if a good night sleep helps employees perform better at their workplace.
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Probability which is the mathematical study of chance, is very fundamental to our everyday life. It is interrelated to statistics and they must be studied together. If a student misses a session of either, it becomes very difficult to understand the next session. We cover all the topics related to probability and statistics such as:
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Sampling Homework Help In PSPP
Sampling is a statistical tool used to determine the observations taken from a large population. There are various methods that can be used in sampling but each depends on the type of research to be carried out. The sample taken should be a representation of the entire population. Sampling is usually applied in many disciplines, like in Marketing; a company may sample their target customers within a particular market niche to find out their buying patterns and how to solve their problems.
The following methods can be used when doing sampling;
- Stratified sampling- This method partitions the target population into groups based on the factor that is to be measured.
- Cluster sampling- in this method, the population is divided into groups called clusters, a random sample is obtained from all possible clusters and then data is obtained on every sampling unit from each of the selected clusters
- Random sampling- In this method each and every member of the population of interest has a chance of making the sample. This is the best method for sampling because it ensures that the entire population is well represented.
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