000			04160nam a2200397 a 4500
005			20250918192246.0
008			130705s2011 enka b 001 0 eng
020			_a9780521883726 (hbk.) _cRM245.40
039		9	_a201401201245 _brosli _c201401021226 _drahah _y07-05-2013 _zrahah
040			_aDLC _cDLC _dDLC _dUKM
090			_aQ180.55.S7K537 2011 3
090			_aQ180.55.S7 _bK537 2011 3
100	1		_aKirkup, Les.
245	1	0	_aData analysis for physical scientists : _bfeaturing Excel / _cLes Kirkup.
250			_a2nd ed.
260			_aCambridge : _bCambridge University Press, _c2011.
300			_axv, 510 p. : _bill. ; _c26 cm.
500			_aRev. ed. of: Data analysis with Excel. 2002.
504			_aIncludes bibliographical references and index.
505	8		_aMachine generated contents note: 1. Introduction to scientific data analysis; 2. Excel and data analysis; 3. Data distributions I; 4. Data distributions II; 5. Measurement, error and uncertainty; 6. Least squares I; 7. Least squares II; 8. Non-linear least squares; 9. Tests of significance; 10. Data analysis tools in Excel and the Analysis ToolPak; Appendixes; Answers to exercises and end-of-chapter problems; References; Index.
520			_a'The ability to summarise data, compare models and apply computer-based analysis tools are vital skills necessary for studying and working in the physical sciences. This textbook supports undergraduate students as they develop and enhance these skills. Introducing data analysis techniques, this textbook pays particular attention to the internationally recognised guidelines for calculating and expressing measurement uncertainty. This new edition has been revised to incorporate Excel{u0CB0}10. It also provides a practical approach to fitting models to data using non-linear least squares, a powerful technique which can be applied to many types of model. Worked examples using actual experimental data help students understand how the calculations apply to real situations. Over 200 in-text exercises and end-of-chapter problems give students the opportunity to use the techniques themselves and gain confidence in applying them. Answers to the exercises and problems are given at the end of the book'-- _cProvided by publisher.
520			_a'Thorough analysis of experimental data frequently requires extensive numerical manipulation. Many tools exist to assist in the analysis of data, ranging from the pocket calculator to specialist computer based statistics packages. Despite limited editing and display options, the pocket calculator remains a well-used tool for basic analysis due to its low cost, convenience and reliability. Intensive data analysis may require a statistics package such as Systat or Origin . As well as standard functions, such as those used to determine means and standard deviations, these packages possess advanced features routinely required by researchers and professionals. Between the extremes of the pocket calculator and specialised statistics package is the spreadsheet. While originally designed for business users, spreadsheet packages are popular with other users due to their accessibility, versatility and ease of use. The inclusion of advanced features into spreadsheets means that, in many situations, a spreadsheet is a viable alternative to a statistics package'-- _cProvided by publisher.
630	0	0	_aMicrosoft Excel (Computer file).
650		0	_aResearch _xStatistical methods _xData processing.
650		0	_aElectronic spreadsheets.
700	1		_aKirkup, Les. _tData analysis with Excel.
856	4	2	_3Cover image _uhttp://assets.cambridge.org/97805218/83726/cover/9780521883726.jpg
907			_a.b15677497 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQ180.55.S7K537 2011 3
914			_avtls003534454
990			_ark4
991			_aFakulti Kejuruteraan & Alam Bina
998			_al _b2013-05-07 _cm _da _feng _genk _y0 _z.b15677497
999			_c550383 _d550383