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Many statistics are based on the assumption that various underlying data or functions of data are normally distributed. For example, the t and F tests are based on this type of assumption; but there are many others.
In practice, many data that may be observed follow approximate normal distributions because they are, in effect, sums of random variables and the central limit theorem comes into play. In other practical situations, functions of the data are known to follow the normal distribution in most cases. For example, in many cases taking the logarithm or the arcsin of data values will yield (approximately) normally distributed values.
Beyond this, it is well known that many statistical procedures perform well even when the underlying distribution is not normal. They are said to be 'robust' and can be safely applied provided that certain conditions are met.
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Differences between descriptive and inferential statistics?
All statistical tests are part of Inferential analysis; there are no tests conducted in Descriptive analysis · Descriptive analysis- describes the sample's characteristics using… o Metric- ex. sample mean, standard deviation or variance o Non-metric variables- ex. median, mode, frequencies & elaborate on zero-order relationships o Use Excel to help determine these sample characteristics · Inferential Analysis- draws conclusions about population o Types of errors o Issues related to null and alternate hypotheses o Steps in the Hypothesis Testing Procedure o Specific statistical tests
What is general statistics?
Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data.[1] Statisticians improve the quality of data with the design of experiments and survey sampling. Statistics also provides tools for prediction and forecasting using data and statistical models. Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, government, and business.^Based On Wikipedia...
How do you figure out the height and distance a confetti cannon will shoot?
By statistical analysis. It is very difficult to calculate these using mechanics. Calculations of the trajectory of a projectile assume that the mass of the projectile is such that air resistance has a negligible effect. This is not the case when the projectile is confetti - even if it is packed densely to start with.
Formula to calculate normality?
The calculation for normality isn't too hard, but you have to have some info before you can find it. You need 1 the number of equivalents. # mol * (subscript on first element of first compound/ number of that element are in the balanced equation) = # equivalents I am probably confusing you on finding equivalents, but there is not much else I can do. sorry Using 0.2489 g. of H2C2O4*2H20 is .00197mol so... .00197 * (2/1) = 3.94*10^-3 equvialents 2 The volume in liters # mL/1000 = #L say 43 ml / 1000 +.043 L so normality would be (3.94*10^-3)/ .043 = 9.16*10^-2 as normality.
What skill are you using if you identify a trend or pattern seen in a graph?
Analysis.
What are some purposes for using a spreadsheet?
To undertake numerical calculations. Accounts, inventory, statistical analysis and statistical forecasting.
What provides the ability to perform a statistical analysis in Excel?
Excel is a spreadsheet and a spreadsheet is a tool for doing numerical analysis and manipulation. So Excel and any other spreadsheet application are ideal for doing statistical analysis. Excel has a huge range of ways of doing statistical analysis. It can be done through simple formulas, like totalling things up. It can be done with the specialised built-in statistical functions. It can be done by using a range of charts. There are lots of other special facilities too.
What are the different types of statistical analyses that can be performed using SPSS?
SPSS allows for a wide range of statistical analyses. If you need SPSS help, you can get professional help from online consultancies like, SPSS-Tutor, Silverlake Consult, etc. and then you can perform various analyses such as descriptive statistics, t-tests, ANOVA, chi-square tests, correlation analysis, regression analysis, factor analysis, cluster analysis, and survival analysis using the software.
How is an epidemic determined mathematically?
An epidemic can be determined mathematically by using statistics. Statistical methods can be utilized for analysis and is often implemented for research.
How do structural models work?
Structural models of the economy try to capture the interrelationships among many variables, using statistical analysis to estimate the historic patterns.
What is prori analysis and posteriori testing of algorithms?
A priori analysis of an algorithm refers to its time and space complexity analysis using mathematical (algebraic) methods or using a theoritical model such as a finite state machine. (In short, analysis prior to running on real machine.) A posteriori analysis of an algorithm refers to the statistical analysis of its space and time complexity after it is actualy run on a practical machine. (in short, anaysis of its statistics after running it on a real machine)
How will you calculate normality using specific gravity?
by bottle method
What are the advantages and disadvantages of using a statistical model?
The importance of statistical modeling is obvious because we often need modelling for the purpose of prediction, to describe the phenomena and many procdures in statistics are based on assumption of a statistical model. Modeling is also important for statistical inference and make decision about population parameter. M. Yousaf Khan
What has the author S Selvin written?
S. Selvin has written: 'Biostatistics' -- subject(s): Biometry, Medical Statistics, Medicine, Research, Statistical methods, Statistics 'Statistical analysis of epidemiologic data' -- subject(s): Data Interpretation, Statistical, Epidemiologic Methods, Epidemiology, Statistical Data Interpretation, Statistical methods 'Statistical tools for epidemiologic research' -- subject(s): Statistical methods, Epidemiology, Epidemiologic Methods 'Modern applied biostatistical methods using S-Plus' -- subject(s): Biology, Biometry, Data processing, S-Plus
What is involved with statistical quality control?
Statistical quality control involves using statistical methods to monitor and improve the quality of products and processes. This includes collecting and analyzing data, setting quality standards, identifying sources of variation, and implementing strategies to reduce defects or errors. Statistical tools like control charts, hypothesis testing, and regression analysis are commonly used in statistical quality control.
Can anyone assist me in using statistical software or programming languages (e.g., SPSS, R, Python) to analyze my data?
Yes, SPSS-Tutor can assist you in using statistical tools like SPSS, R, etc. This is an online platform to help students with distinct statistical tools and software. A professional expert of this platform can assist a student in using SPSS (Statistical Package for the Social Sciences) in several ways: Guidance on software navigation Data input and cleaning Statistical analysis techniques Interpretation of results Troubleshooting and error handling Tips and best practices Customized assistance Overall, a professional expert can provide SPSS help and hands-on guidance, expertise, and support to students using SPSS, enabling them to navigate the software confidently and perform accurate statistical analyses.
Is SPSS used in qualitative data analysis only?
No, SPSS (Statistical Package for the Social Sciences) is not limited to qualitative data analysis only. In fact, SPSS is primarily designed for quantitative data analysis, which involves analyzing numerical data using statistical techniques. It is widely used in fields such as social sciences, psychology, economics, and market research. SPSS provides a range of features and tools for SPSS quantitative data analysis, including: Descriptive statistics: SPSS allows you to calculate and summarize descriptive statistics such as means, standard deviations, frequencies, and percentages. These statistics provide an overview of the distribution and characteristics of your data. Inferential statistics: SPSS offers a variety of statistical tests for making inferences about populations based on sample data. These tests include t-tests, ANOVA (Analysis of Variance), chi-square tests, correlation analysis, regression analysis, and more. Data manipulation: SPSS provides functionalities to manipulate and transform data. You can recode variables, compute new variables, merge datasets, filter cases, and perform various data transformations to prepare your data for analysis. Data visualization: SPSS enables you to create charts, graphs, and plots to visually represent your data. This helps in understanding patterns, relationships, and trends in the data. Advanced statistical techniques: In addition to basic statistical tests, SPSS also supports more advanced techniques. For example, it offers tools for factor analysis, cluster analysis, discriminant analysis, survival analysis, and nonparametric tests.
