Statistical Analysis: FUNDAMENTAL TO STATISTICS.pptx

Statistical Analysis: FUNDAMENTAL TO STATISTICS.pptx

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  FUNDAMENTAL TO STATISTICS Dr. SHAILAJAB ASSOCIATE PROFESSOR
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  INTRODUCTION TO STATISTICS Statistical analysis means investigating trends, patterns, and relationships using quantitative data. It is an important research tool used by scientists, governments, businesses, and other organizations.  To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process. You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.
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  CONTD….  After collectingdata from your sample, you can organize and summarize the data using descriptive statistics. Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings. What’s the difference between descriptive and inferential statistics?  Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population
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  DESCRIPTIVE STATISTICS  Descriptivestatistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.  In quantitative research, after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).  The next step is inferential statistics, which help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.
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  TYPES OF DESCRIPTIVESTATISTICS  There are 3 main types of descriptive statistics:  The distribution concerns the frequency of each value.  The central tendency concerns the averages of the values.  The variability or dispersion concerns how spread out the values are.
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  CONTD…. You can applythese to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.
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  RESEARCH EXAMPLE  Youwant to study the popularity of different leisure activities by gender. You distribute a survey and ask participants how many times they did each of the following in the past year:  Go to a library  Watch a movie at a theater  Visit a national park  Your data set is the collection of responses to the survey. Now you can use descriptive statistics to find out the overall frequency of each activity (distribution), the averages for each activity (central tendency), and the spread of responses for each activity (variability).
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  SUMMARIZE YOUR DATAWITH DESCRIPTIVE STATISTICS  Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.  Inspect your data  There are various ways to inspect your data, including the following:  Organizing data from each variable in frequency distribution tables.  Displaying data from a key variable in a bar chart to view the distribution of responses.  Visualizing the relationship between two variables using a scatter plot.  By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers
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  CALCULATE MEASURES OFCENTRAL TENDENCY  Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:  Mode: the most popular response or value in the data set.  Median: the value in the exact middle of the data set when ordered from low to high.  Mean: the sum of all values divided by the number of values.  However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.
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  CALCULATE MEASURES OFVARIABILITY  Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:  Range: the highest value minus the lowest value of the data set.  Interquartile range: the range of the middle half of the data set.  Standard deviation: the average distance between each value in your data set and the mean.  Variance: the square of the standard deviation.  Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.