Business Statistics help
Statistics falls under applied mathematics often referred as mathematical science that involves organization, analyzation and collection of data in a manner that brings logic. The investigations may fall between inferential or descriptive statistics which represent the two major categories of statistics.
Statistics is also used in collaboration with business courses to collect and analyze data for businesses using different statistical tools like spss .
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Descriptive statistics deals with coming up with data that may be presented in form of tables and graphs without any attempts of drawing inferences as a result. Most characteristics are described in simple terms everyone can understand. Descriptive events will cover day to day events like population data, accidents, prices of goods and services, calamities, sports data and many more.
Inferential statistics on the other hand uses mathematical tools like SPSS, Excel, STATA, R etc. to project and forecast by analyzing the data presented. The findings is used by people employed in various fields like business, Psychology, communications, engineering, social sciences and economics
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There are five main methods used for statistical data. Mean, standard deviation, regression, hypothesis testing and sample size determination.
Mean also referred to as the average is calculated when you get the sum of a list of numbers given divided by number of items given in the same list. Mean helps one to have a rough draft of what to expect of the data that you have as it is easy and quick to calculate. However, where the data comes from skewed distribution from a high number of outliers then the mean may turn to be cumbersome since it not provide accuracy.
Standard deviation is the measure of spread data around the mean and the formula is often written in Greek letter sigma. Standard deviation is a bit more accurate that mean especially when covering widely spread data. However, when covering small data, the outcome will be more of the same as that of the mean since standard deviation is used to determine dispersion of data points. One disadvantage of Standard deviation just like mean, it cannot be relied if taken alone more so for curves that are not normal or large amount of outliers.
Regression is a statistical method used to model explanatory and dependent variables which are plotted on scatterplot. The regression line will show if the relationship is strong or weak. One disadvantage of regression is the fact that one can ignore critical outliers simply because the regression line may reflect ones clients overshadowing the rest. However, most student's avoid topics that cover regression especially in dissertations.
Hypothesis Testing also referred to as t testing is used to asses if a premise is right for your data set or population and is mostly used from business and economics to science and research. This method offers insight in decision making especially after data analysis. However, for this method, the only disadvantage would be watching out for minor errors and common mistakes.
SAMPLE SIZE DETERMINATION
There are tricks and procedures involved in coming up with an accurate right sample size since in most occasions one will not need to collect information from everyone in that population. When conducting sample size determination one should ensure that their data is statistically significant. One disadvantage is using a variable that is not tested in a population may make the study rely on assumptions that may be way off inaccurate. The error will be carried together with your data analysis
We cover Applied statistics help study
Statistics is classified as applied mathematics even though it is also associated with other fields. There are also instances where one may be asked to explain how statistics helps in studying economics
Some of the major topics handled in statistics are; Descriptive Statistics, Graphs and Displays, Probability, Probability Distribution and Population mean
Descriptive Statistics : In descriptive statistics, one is expected to cover descriptive vs inferential, means median and modes, variability, linear transformation, dispersion percentages and position.
Graphs and Displays : Graphs and Displays one is expected to cover histograms, stem and leaf displays, whisker displays, box and five number summaries.
Probability : In probability, one is expected to know variance and standard deviation, expected value, shortcuts for binomial random variables
Probability Distributions : In probability Distributions one is expected to cover normal distributions, poison distributions and binomial distributions.
Population Mean : In population Mean, one is expected to know how to compare two means. Hypothesis test and confidence interval estimates to help them choose sample size and be informed on errors.