Chapter 2: Frequency DistributionsChapter 3: Central TendencyChapter 4: VariabilityChapters 5 and 6: Z-scores and ProbabilityPSYCH 210Exam # 1 Study Guide, Lectures: 1 - 9Chapter 1- What is the difference between a sample and a population? A statistic and a parameter?-A population is the complete set of ALL individuals (or measurements) sharing some common, observable trait. A sample, then, is a subset of observations drawn from a population. -A statistic is a value that refers to a sample, whereas a parameter is a value that refers to the entire population.- What are the main uses of descriptive statistics? Inferential statistics?- Descriptive statistics are used only to describe; that is, to organize or summarize data. Inferential statistics are helpful for comparison of groups, as they allow one to draw conclusions/make inferences extending beyond the immediate data at hand.- What are independent, dependent, and attribute (subject) variables?- The independent variable is the factor manipulated by the experimenter. It can also be how the participants are grouped. The dependent variable is the measured variable. An attribute variable can easily be confused for an independent variable, but it is a variable that the experimenter cannot choose. Attribute variables consist of inherent characteristics (such as gender). You should use the Random Assignment test to determine if you can randomly assign participants into groups; if you can, the variable is a true IV, if you cannot, the variable is an AV.- What are the basic differences between the correlational and experimental methods? - In the correlational method, two different variables are observed to determine whether there is a relationship between them. In the experimental method, one variable is manipulated while another variable is observed and measured. To establish a cause-and-effect relationship between two variables, an experiment attempts to control all other variables to prevent them from influencing the results.- What types of conclusions can you make based on these methods?- Correlation does not equal causation! The results from a correlational study can demonstrate the existence of a relationship between two variables, but they do not provide an explanation for the relationship. An experimental study, in contrast, can lead to conclusions of cause-effect relationships. If the difference is statistically significant, theexperimenter can assume the results are a result of the independent variable impacting the dependent variable.- What is the quasi-experimental method, and how does it differ from the experimental method?- In the quasi-experimental method, there is an Attribute Variable instead of a true Independent Variable (like in the Experimental Method). It is also different from the Correlational Method because the data in a quasi-experimental method is discrete, whereas the correlational method uses continuous data.- What are the four major scales of measurement? How do they differ, and what qualities does each scale possess?- Nominal Data: categorizes/names/labels, arbitrary assignment of numbers- Ex) If 1 = male and 2 = female, 1&2 are examples of nominal data- Ordinal: Categorize, Rank order, Assignment does matter, does not have equal intervals- Ex) If 35s = 1st place, 37s = 2nd place and 42s = 3rd place, 1,2 &3 are Ordinal Data- Interval: Categorizes, Rank orders, Equal Intervals, Does not have absolute zero (Absenceof anything to be measured at 0)- Ex) Temperature (F): 25°, 39°, 106°- Ratio Scale: Categorizes, Orders, Equal Intervals, Absolute Zero- Ex) Race result times: 36s, 37s, 42s- What is the difference between a discrete and continuous variable? Why is this difference important?-A discrete variable consists of separate, indivisible categories. No values can exist between two neighboring categories. For a continuous variable, there are an infinite number of possible values that fall between any two observed values. A continuous variable is divisible into an infinite number of fractional parts. The type of data you have will affect the statistical analyses and graphing you use. Furthermore, discrete data only have apparent limits, whereas continuous data have both real and apparent limits.- Make sure you understand the summation rules covered on the handout from lecture.- ΣXi2 = X12+X22+X32+…XN2 = Square first, sum second-(ΣXi)2=(X1+X2+X3+…XN)2 = Sum first, square second-Be familiar with statistical symbols covered thus far (for example, , , ), and what each is used for.-population mean-= population standard deviation-= Summation -M = Sample Mean-s2 = Sample Variance-σ2 = Population VarianceChapter 2: Frequency Distributions- What is the difference between a simple and a grouped frequency distribution?-A simple frequency distribution has two columns: x(raw scores) and f (frequency), with the raw scores rank ordered from highest to lowest. The Grouped frequency distribution condenses the information so that it’s easier and faster to read, but in that process, we lose details. (For example, we can no longer discern exactly which score is highest or lowest.)- Be able to set up a simple frequency distribution and a grouped frequency distribution.- SFD: Be sure to include ALL x values, even if they ‘don’t exist in data set,’ display x with a frequency of 0. Rank order raw scores with lowest beginning at bottom of table- GFD: Determine class width by dividing total number of rows (highest-lowest x) by Number of desired Class Intervals (normally 10)- What are real limits, and when do we have to be concerned about using them? How are real limits different from apparent limits?- Real Limits are used for continuous data. To calculate a real limit, determine the unit of measurement used and divide it in half. The real limits are ½ a unit above to ½ a unit above the actual numerical value. This is different than apparent limits, whose limits are just the same as the given numerical value.- What is the difference between a bar graph and a histogram? When is each used?- Bar graphs have gaps in between their bars whereas the bars in histograms touch each other. Bar graphs are used with discrete data (usually non-numerical values, or data using the nominal or ordinal scale) while histograms are for continuous data (or data of interval or ratio scales).- What are some ways