Understanding Business Research Terms and Concepts Part 2

Each statistic in the descriptive form lowers the quantity of data into a much simpler summary.

Descriptive statistics are digits that are used to summarize and describe a given range of data (Klenke, 2008). Basic descriptive data includes, mean, median, mode, variance and standard deviation. The data can be rearranged in an ascending order as follows:

the audience, and it is hard to put it to perspective. Therefore, a statistic is appealing to the

The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than a population, it is convenient, saves time, energy and money because n is smaller than population and above all it helps to control systematic and random errors. When we are making a conclusion, we should have a certain confidence or probability of being right and that is called the alpha level; which the risk you are willing to

A statistic is a numerical summary of a sample and a parameter is a numerical summary of a population.

The mean (x) is a measure of _central__________ __tendency___________ of a distribution while the SD is a measure of __dispersion___________ of its scores. Both x and SD are _descriptive___________ statistics.

Descriptive designs -the current state of affairs, Provides a relatively complete picture, does not assess relationships among variables Experimental - impact on experimental manipulations on a dependent variable, Allows drawing of conclusions, Cannot experimentally manipulate many important variables

spread of scores from the mean (Burns & Grove, 2007). The larger the value of the standard deviation for study variables, the greater the dispersion or variability of the scores for the variable in a

Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire population or a sample of it. Descriptive statistics are broken down into measures of central tendency and

There are ten things stats is very important for such as: weather forecasts, emergency preparedness, predicting diseases, medical studies, genetics, political campaigns, insurance, consumer goods, quality testing, and the stock market. Weather forecasts use stats to predict the weather using prior conditions, the weather forecasts tells us how to prepare for the day and what to expect throughout the day. Emergency teams use the weather forecasts to tell them to be ready or not to help people they use statistics to tell them when danger may occur. When predicting disease the lady or man telling you about the deaths or the disease is spreading it may not

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

The last few weeks we have been covering descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than population,

“Statistics is a mathematical science concerned with the collection, presentation, analysis and interpretation or explanation of data.” (Black et. al, 2013).

Descriptive Analytics, probably the most common type of analytics, is the process of describing and evaluating the historical data and recognizing patterns from samples. It serves as a foundation for more advanced analytics. An example of Descriptive Analytics would be discovering and reporting trends.

Descriptive research attempts to elucidate characteristics of an object or phenomenon, without focusing on possible antecedents to that object or phenomenon. A descriptive researcher cannot determine the ‘cause and effect’ relationships that most experimental scientists aim to uncover (Knupfer, 2001). Descriptive designs often involve an investigator or investigative team that records the ‘qualities’ of what they are studying (e.g. a subject’s mood, the color of an object), but they are not restricted to recording ‘qualities’. They can also use quantitative (i.e. numerical) methods, such as using inferential statistics to find correlations between survey answers, but this use of statistics is often specific to survey studies rather than case studies.