They will instead make a cash settlement, which reflects the market value at the time the loss happened. This is so a prospective buyer knows a vehicle was previously written off when conducting vehicle history checks. These checks also cover whether the vehicle is stolen or has outstanding finance, too. So, what do the categories mean?

For example, Tufte, -- and then give the complete citation under "References" at the end of the paper:. This section should translate the intellectual concerns expressed above into your research. Indicate here the nature and source of your data i. For example, do you expect the hypothesized relationship to hold across sex and race for individual-level data or across types of political systems for national-level data? You must also formalize your hypotheses in this section.

By formalize, I mean physically distinguish your hypotheses from the rest of the text in two ways: 1 labeling them as H1, H2, etc. For example, you might say, "This leads to our first hypothesis:. Hypotheses should be bold assertions of expectations that lend themselves to falsification.

They gain in credibility as they survive attempts to test them -- i. Admittedly, it is intellectually more satisfying to propose hypotheses that are supported rather than falsified through data analysis. Whether your hypotheses are supported or falsified will have no effect on the paper's grade. Whenever possible, formulate directional hypotheses, which invite falsification more readily than non-directional hypotheses. We will discuss the difference between the two soon.

Also pay attention to the linkage between the concepts in your theory and in the way you operationalize those concepts in formulating your hypotheses. Be careful not to throw away data by collapsing variables to do crosstabulations when they might more properly be analyzed instead through correlational and regression analysis.

For example, the "thermometer" variables in the VOTE88 data are expressed from 0 to , while those in VOTE96 are collapsed into a few ordinal categories. Report here the results of your statistical tests. Refer explicitly to the hypotheses being tested by number: H1, H2, and so on. In most cases, your data should report tabulations of statistics. If you use ordinal or continuous data, your statistics will involve correlation coefficients, regression coefficients, or results of t-tests or F-tests.

Do not simply accept and report the format of SPSS computer printout. That's not very classy. Instead, reformat the data into tables like those in the Journal of Politics or someother professional journal. Take some care in reporting your tables. Provide informative titles. Be sure to include the Ns on which any percentages are based. We will deduct points if Ns are not included.

Statistical tables should contain all the information that the reader needs to analyze the test. Your job as writer is to point out the key features of the analysis, not to repeat all the numbers in the tables. The data are in the table; the text should be used to summarize its particulars. Example: "All but one of the correlations in Table 1 are in the expected direction and are statistically significant.

Please report correlations and slopes if you employ regression analysis only to the second decimal point. Do not slavishly reproduce them to the ultimate decimal point from the SPSS output. If your analysis involves plots, you may use the PLOT printout if you label it properly and mount it on a page in your paper with aesthetic feeling. Where possible, avoid reference to variables by their SPSS labels e. Instead, refer to them in more descriptive terms: "percent black" and "vote for Clinton in This section should return you to the problem raised at the beginning of the paper.

It provides the link between your narrow data analysis and the broader intellectual concerns with which you began. You might start by summarizing the results of your statistical tests and determining whether your research supported or contradicted prevailing theory.

If your hypotheses are supported, how powerful is the theory? That is, how much variance are you explaining in the dependent variable? If your research fails to support the theory tested, what are the possible sources of failure? The theory itself? The presence of confounding variables?

The inadequacy of the data or the way the variables were measured? The basic research design? Statistics , Followers. Papers People. We estimated the mortality from various diseases caused by cigarette smoking using two methods and compared the results.

The other method, suggested by R. Peto et al Lancet ; , requires data on mortality from lung cancer among people who have never smoked and among smokers, but it does not require data on the prevalence of smoking. The differences between the two methods are small and appear to be explicable. The Prevent model can be used for more general scenarios of effective health promotion, but it requires more data than the Peto et al method, which can be used only to estimate mortality related to smoking.

Save to Library. Nonlinear error correction models. Analysing necessity and sufficiency with Qualitative Comparative Analysis: how do results vary as case weights change? Improving small area estimation by combining surveys: new perspectives in regional statistics. Tools for evaluating EU agricultural policies: an integrated approach. This approach should also allow to evaluate the This approach should also allow to evaluate the adjustment capability of farms as a function of the characteristics of different territories.

The proposed methodology is illustrated by an example using data relative to the 41 provinces of Northern Italy. The exact distribution function of the ratio of two dependent quadratic forms. On linear predictors for non-stationary ARMA models. It was shown Niemi, that the prediction formulas applied in the stationary ARMA models are valid also when the noise process is non-stationary with the variances uniformly bounded from below and above.

We note that this We note that this statement can be extended to the Controlling the number of false discoveries: Application to high-dimensional genomic data. Researchers conducting gene expression microarray experiments often are interested in identifying genes that are differentially expressed between two groups of specimens. A straightforward approach to the identification of such Slope distribution in front-back asymmetric stochastic lagrange time waves.