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Example of descriptive statistics essay
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Descriptive statistics
What is descriptive statistics? Usually under descriptive statistics summative methods of description the data in succinct ways is considered. Data analysis usually begin with descriptive statistics, because it helps to understand what data we have – what is the sample, what is the accuracy of the data and how it is possible manage it.
The analysis of data usually begins with such descriptive statistics as the mean, median, mode, variance and standard deviation. Mean, median, and mode are known as measurements of the central tendency of the data and range, standard deviation, variance give the information about the variability of the data. The mode should be preferably used when calculating measure of center for the
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Variable names are:
PRICE = Selling price ($ hundreds)
SQFT = Square feet of living space
AGE = Age of home (years)
FEATS = Number out of 11 features (dishwasher, refrigerator, microwave, disposer, washer, intercom, skylight(s), compactor, dryer, handicap fit, cable TV access
NE = Located in northeast sector of city (1) or not (0)
COR = Corner location (1) or not (0)
TAX = Annual taxes ($)
Where variables “Age” and “Tax” have missing values. In this work I will not touch the question of missing data management techniques, because it is different and very broad topic. And for the simplicity reasons I will delete cases with missing values for the variable “Age” from the analysis, and not use variable “Tax” for the further analysis.
Cleared data file consists of 68 cases (see appendix 1).
Mean
The mean of a data set is the arithmetical average of all the numbers. Its formula is:
where n is number of cases.
One problem with using the mean, is that if there is one outcome that is very far from the rest of the data (outlier), then the mean will be strongly affected by this outcome. There is possibility to low down the effect of outliers. This method is called the trimmed mean. The idea of the trimmed mean is to get rid of the outliers or 5% on the top and 5% on the
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
A researcher determines that 42.7% of all downtown office buildings have ventilation problems. Is this a statistic or a parameter; explain your answer.
I have four quiz scores: 94, 93, 85, and 0. what is the mean (average) of my quiz scores? Would you code this as a factor or a numeric value in R?
The extent to which a distribution of values deviates from symmetry around the mean is the skewness. A value of zero means the distribution is symmetric, while a positive skewness indicates a greater number of smaller values, and a negative value indicates a greater number of larger values (Grad pad, 2013). Values for acceptability for psychometric purposes (+/-1 to +/-2) are the same as with kurtosis.
This method is used since it is the most appropriate for calculating the mean and the standard deviation of a grouped data.
So, for figure 5 which is the means plot, we use the means plots to see if our mean will be different with the groups of data. Because when we are ale to see the visual interpretation of this section, we will come to the following conclusions, which we can the mean scores for the section that is higher than our mean scores for the section 2 and 3.
After reading this book, I am touched by the underlying philosophy of statistics. Various theories and models are introduced in this book. During the progress of the development, controversies and confits among these theories are largely attributable to diversity of ideology and doctrine from their establishers. In future, the statistics might evolve into a new era and the vogue methods, like p-value or confidence interval, might be discarded. I am looking forward to witness how statistics make our lives
Furthermore, the methods applied convey “the techniques or procedures used to gather and analyze data that is
Many statistical ideas were mentioned in the Barron’s guide. In the topic called Graphing Display the Barron’s guide discusses the different types of graphs, measures of center and spread, including outliers, modes, and shape. Summarizing Distributions mentions different ways of measuring the center, spread, and position, including z-scores, percentile rankings, and the Innerquartile Range, and its role in finding outliers. Comparing Distributions discusses the different types of graphical displays and the situations in which each type is most useful or appropriate. The section on Exploring Bivariate Data explains scatter plots in depth, discussing residuals, influential points and transformations, and other topics specific to scatter plots. Conditional relative frequencies and association, and marginal frequencies for two-way tables were explained in the section entitled Exploring Categorical Data. Overview of Methods of Data Collection explained the difference between censuses, surveys, experiments, and observational studies. Surveys are discussed more in depth in Planning and Conducting Surveys, including characteristics of a well-designed and well-conducted survey, and sources of bias. Planning and Conducting Experiments explains experiments in depth; going over confounding, control groups, placebo effects, and blinding, as well as randomization. Basic rules for probability are discussed in Probability as Relative Frequency, including the law of large numbers, addition rule, and multiplication rule. Other topics discussed in this section include the different types of probability calculations. Combining Independent Random Variables discusses manners in which two variables can be compared to each other and things to be wary of while doing so.
Broadly, statistics is a set of disciplines for study quantitative information. Implied that several methods used to collect or process or interpret quantitative data from large amount of information, then finally generate a calculated number, for example average, mean, standard deviation…etc. All of these are the key reference for decision making or predicting consequences. Thus, it enables us to estimate the extent of our errors.
Standard deviation is an estimate of variability that accompanies the mean in describing a distribution. You are taking a look at each distribution to see how far away each score deviates from the mean.
The first table was titled Other Measures. It provided information on the sample size, minimum, maximum, first quartile, third quartile, given percentage, and value of percentile. These values are used to compute range and interquartile range in the measures of dispersion. The last table shows the mean plus or minus 1, 2, or 3 times the standard deviation and offers details on how many values fall within the ranges created by those calculations.
The mean is usually used as a measure of central location. However, the average is extraordinarily sensitive to abnormally large or small observations (Anderson et al., 2011, p.90). When using data with extreme values, the median is desired because its calculation depends less on the broadness of the rang...
As a population, we are bombarded with percentages and statistics, but how does one know if what we are being told is correct? The book How to Lie With Statistics by Darrel Huff was written to help readers better understand statistics especially when they are presented to us in ways that can be misleading or misunderstood. The book is not meant as a guide on how to change or manipulate statistical numbers. However, if statistics are not presented properly or perhaps purposely misleading people, this book will help readers question or form their own opinions from data. Most people simply are not that interested when you hear the word statistics and many times people do not believe the numbers presented. This mistrust occurs most often for two reasons: the person not being able to see the raw data and where or how it was collected and the person not being able to verify the credibility of the information presented. Throughout the book, Huff discusses different statistical techniques that can be used improperly and how one can discern good statistics from those that may have been manipulated.