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is a reasonable test statistic for testing {3J = o. Now we need to know how to obtain est.SD(P3). In simple least-squares regression, Var(p) = (72 /L(x i - .i=)2. For the acid content data, this formula yields Var(p) = 0.0000203944(72. Note that 0.0000203944 appears in the lower right corner of the matrix (X' X) - 1 in the subsection on simple regression in Section 3.7. In fact, in simple regression, Var(p) can always be obtained by multiplying (72 by the second diagonal entry in (X' X)-l. Similarly, in multiple regression with four explanatory variables, Var(P3) can be obtained by multiplying (72 by the fourth diagonal entry in (X'X)-l. In the general linear regression model, Var(p) is equal to (72 times the (j + I)th diagonal entry in (X' X)-l for any j = 0,1, ... , p. Estimating (72 by &2 = SSRfull/(n - 5), we obtain est.Var(p,), and taking the square root, we obtain est.SD(PJ). When {33 = 0, t has a t distribution with n - 5 degrees of freedom, which are the degrees of freedom associated with &2. For the turnip green data, = - 0.5026 and est.SD(PJ) = 0.2109, so It I = 0.5026/0.2109 = 2.38. The p-value is 0.03. Note that the p-values are the same for the F test based on a comparison of models and for the t test based on In fact, the two tests are equivalent and their test statistics are related by the equation F = t 2. For the turnip

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Programming Projects 1311 Modify the C h a r c o u n t e r class to use an array of i n t s instead of a map, and explain whether this modification is likely to affect the program's running time Incorporate the change and measure the running time on a large data file for both compression and uncompression 1312 Storing the character counts in the encoding table gives the uncompression algorithm the ability to perform extra consistency checks Add code that verifies that the result of the uncompression has the same character counts as the encoding table claimed 1313 Describe and implement a method of storing the encoding table that uses less space than the trivial method of storing character counts

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1314 Add the robust error checks for the compression program suggested at the end of Section 1313 1315 Analyze empirically the performance of the compression program and determine whether its speed can be significantly improved If so, make the required changes 1316 Split the Tokenizer class into three classes: an abstract base class that handles the common functionality and two separate derived classes (one that handles the tokenization for the balanced symbol program, and another that handles the tokenization for the crossreference generator)

Generate an index for a book The input file consists of a set of index entries Each line consists of the string IX:, followed by an index entry name enclosed in braces and then by a page number enclosed in braces Each ! in an index entry name represents a sublevel A I ( represents the start of a range and a I ) represents the end of the range Occasionally, this range will be the same page In that case, output only a single page number Otherwise, do not collapse or expand ranges on your own As an example, Figure 1333 shows sample input and Figure 1334 shows the corresponding output

Table 1.2 Array of Data Consisting of n Observations of a Response Variable and p Explanatory Variables Observation Number

IX: IX: IX: IX: IX: IX : IX: IX: IX: IX: {series ( 1 {2 ) {series!geometricl ( } {4) {Euler's constant} {4) {~eries!geometricl)} { 4 3 {~eries!arithmetic((} 14) {~eries!arithmeticl)} {5) {~eries!harmonicl 1 ( 153 {Euler'sconstant) {5) {~eries!harmonic( ) } {5} {Series 1 ) 1 {5}

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Euler's constant: 4, 5 Series: 2-5 arithmetic: 4-5 geometric: 4 harmonic: 5

1318 Use a map to implement a spelling checker Assume that the dictionary comes from two sources: one file containing an existing large dictionary and a second file containing a personal dictionary Output all misspelled words and the line numbers on which they occur (note that keeping track of the misspelled words and their line numbers is identical to generating a cross-reference) Also, for each misspelled word, list any words in the dictionary that are obtainable by applying any of the following rules a Add one character b Remove one character c Exchange adjacent characters

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The original paper on Huffman's algorithm is [3]Variations on the algorithm are discussed in [2] and [4] Another popular compression scheme is Ziv-Lempel encoding, described in [7] and [6] It works by generating a series of fixed-length codes Typically, we would generate 4096 12-bit codes that represent the most common substrings in the file References [ I ] and [ 5 ] are good surveys of the common compression schemes

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