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Saturday look for a complex array of selections in all sports. Make sure you follow us throughout the day as the line changes evolve, then we will come in hard. FAMOUS EARLY EASTER EDGE UP NOWThe Charlotte Hornets are broken right now. They just lost outright at home as double-digit favorites to the Bulls last night.

The Lakers will be playing just their 2nd game in 6 days and will have plenty of energy. They are coming off a 107-104 upset win in Philadelphia as 8. The Lakers are 5-0 ATS in their last five meetings in Charlotte.

The underdog is 20-6-1 ATS in the last 27 meetings. Give me the Lakers. I had my eye on the Lakers in this matchup after they pulled out that big road win at Philadelphia on Thursday.

That's the kind of win that can get a team going. More than anything, I'm not a big fan of this Hornets team. Charlotte is just 1-6 in their last 7 and are in a brutal spot here playing on no rest after an overtime game last night, where they lost to a Bulls team that's arguably the worst team in the league. With Kaminsky and Zeller both out with injuries and Jeremy Lamb questionable with a ankle injury, I just don't think Charlotte will have enough gas in the tank to be competitive here.

Note that all 5 starters logged at least 33 minutes, with 4 of the 5 racking up at least 42. I'll take the points, but I really like LA to win here outright. They just ended their 10-game losing streak with a 119-111 (OT) victory at Charlotte.Specifically, often the n's in individual cells are not equal because the assignment of subjects to those groups typically results not from an experimenter's manipulation, but from subjects' pre-existing dispositions.

If, in spite of the random selection of the entire sample, the n's are unequal, then it may suggest that the independent variables are related. For example, crosstabulating levels of independent variables Age and Education most likely would not create groups of equal n, because education is distributed differently in different age groups.

If you are interested in such comparisons, you can explore specific frequencies in the breakdowns tables, trying different orders of independent variables. However, in order to subject such differences to statistical tests, you should use crosstabulations and frequency tables, Log-Linear Analysis, or Correspondence Analysis (for more advanced analyses on multi-way frequency tables).

Graphs can often identify effects (both expected and unexpected) in the data more quickly and sometimes "better" than any other data analysis method.

Categorized graphs allow you to plot the means, distributions, correlations, etc.

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The graph below shows a categorized histogram which enables you to quickly evaluate and visualize the shape of the data for each group (group1-female, group2-female, etc. The categorized scatterplot (in the graph below) shows the differences between patterns of correlations between dependent variables across the groups. Additionally, if the software has a brushing facility which supports animated brushing, you can select (i. Frequency or one-way tables represent the simplest method for analyzing categorical (nominal) data (refer to Elementary Concepts).

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They are often used as one of the exploratory procedures to review how different categories of values are distributed in the sample. In practically every research project, a first "look" at the data usually includes frequency tables.

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For example, in survey research, frequency tables can show the number of males and females who participated in the survey, the number of respondents from particular ethnic and racial backgrounds, and so on. Responses on some labeled attitude measurement scales (e.

Customarily, if a data set includes any categorical data, then one of the first steps in the data analysis is to compute a frequency table for those categorical variables.

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Purpose and Arrangement of Table. Crosstabulation is a combination of two (or more) frequency tables arranged such that each cell in the resulting table represents a unique combination of specific values of crosstabulated variables. Thus, crosstabulation allows us to examine frequencies of observations that belong to specific categories on more than one variable. By examining these frequencies, we can identify relations between crosstabulated variables.

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Only categorical (nominal) variables or variables with a relatively small number of different meaningful values should be crosstabulated. Note that in the cases where we do want to include a continuous variable in a crosstabulation (e. The simplest form of crosstabulation is the 2 by 2 table where two variables are "crossed," and each variable has only two distinct values.

MALE FEMALE FEMALE FEMALE MALE. A B B A B. The resulting crosstabulation could look as follows. In general, this table shows us that more females than males chose the soda pop brand A, and that more males than females chose soda B. Thus, gender and preference for a particular brand of soda may be related (later we will see how this relationship can be measured).

The values in the margins of the table are simply one-way (frequency) tables for all values in the table. They are important in that they help us to evaluate the arrangement of frequencies in individual columns or rows. Thus, the differences between the distributions of frequencies in individual rows (or columns) and in the respective margins informs us about the relationship between the crosstabulated variables.

Column, Row, and Total Percentages. The example in the previous paragraph demonstrates that in order to evaluate relationships between crosstabulated variables, we need to compare the proportions of marginal and individual column or row frequencies.


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