The relationship between marginal revenue and total revenue is the change in total revenue with respect to the variable change in quantity. Marginal revenue = demand MR = d(TR)/dQ, where Q is quantity. For each additional unit of output sold total revenue increases but only by the amount equal to the marginal price of the output unit. • As we increase the number of units sold which generate a positive marginal revenue, the total revenue increases (The total revenue increases when marginal revenue is positive) • When marginal revenue is zero the total revenue does not change and the total revenue is maximum (When MR = 0, TR Δ = 0) B. Define marginal cost Marginal cost is the total cost to produce an additional unit of goods sold.
One way to do this is by narrowly defining the sampling criteria to make the sample as homogeneous (or similar) as possible to control for extraneous variables. Other methods include randomization or random assignment of subjects to groups; matching subjects on extraneous variables and then assigning them randomly to groups; application of statistical techniques of analysis of covariance; and balancing means and standard deviations of groups (Mcleod, 2008). The amount of control that the researcher has over the variables being studied varies, from very little in exploratory studies to a great deal in experimental design, but the limitations on control must be addressed in any research proposal (Silverstein,
is biased, so we could make incorrect conclusions about model fit Detecting Heteroskedasticity: 1. Plot the regression residuals/errors, the “ehats,” or the squared residuals, the "ehats-squared", against the X variables (you should plot the residuals against each X variable separately to check which of the X variables might be a source of Heteroskedasticity). a. If Heteroskedasticity is not present, the variation in the ehats around (above and below) zero will be the same for all values of X. Figures 1a and 1b below are examples of residual plots when Heteroskedasticity is NOT present.
Examine the reasons why some sociologists choose to use questionnaires when conducting research (20 marks) Questionnaires are surveys which are sent out to the public with pre-set questions which are usually closed-ended, and they are defended by positivist’s sociologists who favour using them over qualitative methods as questionnaires meet the main positivistic goal of reliability. Questionnaires are reliable because every questionnaire within a pack will be identical. So then this should generate similar answers. It also shows that if there is large differences it shows that it is because that is what the public think, not because the questions are worded differently for different people. Also this can rule out fluke answers which can be ignored.
Extraneous Variables are undesirable variables that influence the relationship between the variables that an experimenter is examining. They exist in all studies and can affect the measurement of study variables and the relationships among these variables. Because of this, they are of primary concern in quantitative studies because they can interfere with obtaining a clear understanding of the relational or causal dynamics within these studies (Burns & Grove, 2011). Another way to think of it is they are variables that could possibly influence the outcome of an experiment, though they are not the variables of actually of interest. A major goal in research design is to decrease or control the influence of extraneous variables as much as possible.
In presenting and analysing empirical evidence such as Howe (1997) supporting the thesis that intelligence can, in fact, change under the right conditions and given enough time, a strong indication of malleability is provided. Especially the Head Start initiative in the U.S.A. has indicated changeability as well as severely deprived orphans, who are adopted into stimulating and caring environments, have shown remarkable mental and physical catchup. Furthermore, an increase in IQ scores over generations in several countries has been found by the researcher J.R. Flynn, which also hints at changeability. Criticisms of malleability, which support the genetic approach and immutability, were presented and discussed as well as essential limitations to the genetic approach and its evidence, since it seemed that a strong genetic influence on intelligence exists but the environment cannot be disregarded. The sociological impact that research within the area of intelligence has had on political decisions was illustrated to show how the media can influence the impressionable public opinion, which might inhibit or derail further research.
Nevertheless I was surprised to stumble upon a Facebook meme suggesting that people who wreck their brains worrying everyday have a higher IQ than the others. Intrigued by this little discovery of mine I did some research. Firstly, there is an evolutionary link discovered by scientists between our tendency to worry and our intelligence, meaning that our capacity to worry has evolved with our ability to think. Prove for this is our history itself. By the passing of centuries people have become more concerned about society and the way we conduct life.
It’s said that personality traits are not the only factors that control an individual’s behavior; situations are important as well. Some situations will make a person more or less shy, open, careful or friendly, and more or less dominate this is because situations are varied according to the people who are present and implicit rules that apply (Prince & Bouafford. 1974: Wagerman & Funder, 2007. Funder pg 118) The dominate and the submissive for individual differences has zero value for a trait only a continuum at measuring how people differ from one another. The trait approach assumes that people are their traits, yet dominate and submissive shows that people are unpredictable and it’s all conditional on what is going on at the time of the situation.
Specific variances – actual figures v expected figures – this includes • Actual v budget • Actual v Forecast It is unlikely that you will be perfect at budgeting (unless you have a crystal ball), so you are bound to get variances at least occasionally. Generally, small variances are simply part of doing business, large variances need investigating – but don’t get complacent; think of it like testing for lumps, or checking moles – you need to get a foundation of what is NORMALLY a bit over or a bit under before you can understand WHAT IS ODD… and this only comes with practice. Trend variances – small, continual changes over time, that incrementally diverge from expected. • this month’s v last month’s • August 2010 v August 2011 – both actual and budgets What can seem normal, can seem so because we are used to it. Trend analysis is a bit like watching your weight; when you check your scales each day, it only seems like tiny changes, but if you look at this birthday compared to your weight last birthday that is when you notice the few extra kilos have snuck on … Trend analysis puts a spotlight on the changes that creep up on us little by
Uncertainty in response rates and high probability of double entries which will introduce high significant error in the sampling and subsequently impair the quality of data. • Subjectivity in the Interview. These will be mitigated by well-organized questionnaire and the inherent subjectivity or Biasness can be mitigated by well –structured training for the data collectors and respondent by using a standardized interview protocol. In addition, technology and solid program can be used to mitigate this. Examples of such are computer-assisted survey information collection (CASIC) which brings onboard higher data quality , privacy of the respondent and error elimination during data processing.